Mental Lexicon
Running head: SYSTEMATICITY IN THE MENTAL LEXICON
Exploring systematicity in the mental lexicon
Richard Shillcock*, Simon Kirby*, Scott McDonald*, Chris Brew†,
*
University of Edinburgh, Scotland
†
Ohio State University, USA
Corresponding author:
Dr. Richard Shillcock
School of Philosophy, Psychology, and Language Sciences
University of Edinburgh
7 George Square
EH8 9JZ
Tel: +44 131 650 4425
Email: rcs@inf.ed.ac.uk
17,606 words
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Mental Lexicon
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Abstract
We present a comprehensive analysis of systematicity in the English lexicon.
Systematicity is one of the brain's most successful and pervasive representational
strategies. We report statistical analyses of a 100 million-word corpus of contemporary
English, in which we quantify “semantic” distances between words in terms of context
vectors. We then compare these semantic distances between each word and every other
word with the corresponding phonological distances, to show significant systematicity
in the relationship between form and meaning: words that sound similar tend to have
similar meanings, within the constraints dictated by the size of the adult lexicon. We
explore the implications for language processing by comparing the systematicity
dimension with other dimensions known to affect lexical access, and we develop a new
perspective on the role of pragmatically important words that are pivotal to the
systematicity of languages like English, both in the normal and the impaired mental
lexicon.
Keywords: mental lexicon, phonological form, word meaning
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Exploring systematicity in the mental lexicon
Why do English words sound the way they do? The sound /k æ t/ evokes the
meanings associated with cats, but there is nothing particularly feline about the sound
itself; this observation is Saussure’s “arbitrariness of the sign” (de Saussure; eds. Bally
& Sechehaye, 1916). The essential arbitrariness of the relationship between sound and
meaning goes against the pervasive structure that we find at every level of linguistic
description: there is systematic, compositional structure in the phonology, morphology,
syntax, semantics and pragmatics of natural languages. It is reasonable to ask whether
the words in the monosyllabic, monomorphemic core of the lexicon – cat, think, chair,
has, … – are as atomic and arbitrary as they first appear. In this paper we exploit the
fact that it is now possible to use very large corpora to define words syntagmatically as
points in a high-dimensional space; such definitions of words can be used as semantic
definitions in modelling psychological data (Landauer & Dumais, 1997; Lund, Burgess
& Atchley, 1995; Lund & Burgess, 1996; McDonald, 2000a) and allow us to specify the
“semantic” distance between any two words in the space. We can also specify the
phonological distance between any two words in terms of the number of phonological
features that must be changed to turn any one word into another. Thus, for the first time,
it is possible to explore exhaustively the contours of the quantitative relationship
between form and meaning in a substantial part of the lexicon of a particular language.
Below, we report the structure that becomes apparent from this perspective, and we
assess the psychological implications for theories of lexical storage and lexical access in
speech processing1.
The question of how words are stored in the mental lexicon – the brain’s
repository of lexical knowledge – has attracted a large volume of research in recent
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decades. A number of powerful paradigms have been developed for studying speech
production and perception. Several major themes have emerged:
(a) The role of phonology. There have been strong claims to the effect that the
phonological form of words determines the functional structure of the mental lexicon.
For instance, Fay and Cutler (1988) point out that in malapropisms the target word (e.g.
monotony) and its erroneous substitute (e.g. monogamy) tend to resemble each other in
their initial segments, number of syllables and stress pattern. They conclude that there is
a single mental lexicon organised for speech perception and “cross-wired” for
production. Thus, the exigencies of spoken word recognition take precedence, being the
aspect of language processing that is least under the individual listener’s control. A
second claim for the priority of phonological representations comes from those
researchers who argue that the point at which a word is first learned – its Age of
Acquisition – conditions its processing in adulthood (see, e.g., Brown & Watson, 1987;
Gilhooly & Logie, 1982). In this view, the first words to be stored in the mental
lexicon’s phonological space are at an advantage compared with later arrivals, which
must accommodate themselves to the presence of the earlier words.
(b) The role of word meaning. Category specific impairments, in which the processing
of a particular semantic class of words such as tools, furniture, or fruit may be
disproportionately impaired, perhaps constitute some of the most provocative evidence
concerning the organisation of the lexical and conceptual knowledge (see, e.g.,
McKenna & Warrington, 1993; McNeil, Cipolotti & Warrington, 1994). Connectionist
models have been presented in which feature-based semantic representations allow such
effects to emerge, either as the result of sparser coding for certain classes of word (Plaut
& Shallice, 1993) or because the lexicon organises itself topographically, so that similar
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words are grouped together in a semantically organised physical space (Miikkulainen,
1997). Unlike Fay and Cutler’s earlier proposal for single, phonologically arranged
lexical entries, this last proposal involves separate systematic coding of semantic and
phonological information.
(c) Localist and distributed representations. Earlier models of the mental lexicon
typically involved localist lexical representations, in which a word was stored at an
individual address or node (Forster, 1976; Morton, 1969). Over the last fifteen years, the
lexicon has also been modelled in terms of distributed mappings between orthographic,
phonological and semantic representations (Hinton & Shallice, 1991; Plaut & Shallice,
1993; Plaut, McClelland, Seidenberg & Patterson, 1996; Seidenberg & McClelland,
1989). Such connectionist models involve superpositional storage: all the lexical
information of a particular type is stored across the same representational substrate. In
this sense, an individual word’s role in the lexicon is constrained by all of the rest of the
words in the lexicon. The notion of a single linguistic entity being defined in relation to
all other such entities also emerged many years earlier in the structuralism developed by
Saussure, but without a concern for processing behaviour and without any
computational implementation.
(d) Lexical neighbours. When researchers have approached the issue of interference or
facilitation between lexical representations, the relationships studied have
overwhelmingly been between close neighbours in lexical space, such as might exist by
changing one segment in a spoken word or one letter in a written word (Coltheart,
Davelaar, Jonasson & Besner, 1977; Luce & Pisoni, 1998). Sometimes these neighbours
have shared a rime (e.g., Marslen-Wilson, Moss & vanHalen, 1996), a word beginning
(Marslen-Wilson & Welsh, 1978), or a sequence of segments representing the whole of
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the smaller word (McQueen, Norris & Cutler, 1994; Shillcock, 1990), but the two words
in each relationship have typically sounded similar in a clear, intuitive sense.
Demonstrations of interactions between such words have allowed researchers to make
inferences about the functional architecture of the mental lexicon.
(e) Lexical categories. Words fall into different syntactic categories: noun, verb,
adjective, adverb, preposition, pronoun, and so on. These categories themselves fall into
two broader types: function words and content words. This latter distinction has
attracted a large volume of research built around the observation that function words
and content words may be differentially impaired, as in Broca’s aphasia (Goodglass &
Kaplan, 1972; Menn & Obler, 1990), and differentially processed in normal speaking
and listening (Cutler, 1993) and reading (Chiarello & Nuding, 1987). Such differences
may partly depend on physical distinctions between the two word types; in English,
function words tend to be shorter, more frequent and less acoustically prominent than
content words. However, function words are also seen as being more closely involved in
the articulation of syntactic structure (Cann, 2000), and seem to be better processed in
the left hemisphere (Mohr, Pulvermüller & Zaidel, 1994). Significant distinctions in
typical phonological form have also been observed between different types of content
word: for instance, in English, nouns tend to contain more nasals than verbs, whereas
verbs tend to contain more front vowels than nouns (Sereno, 1994). All of these
differences between lexical categories suggest possible large-scale distinctions in the
functional and even the physical architecture of the mental lexicon.
(f) Lexical variables. There has been a consistent interest in developing and testing
individual dimensions of lexical variation, such as word frequency (Howes & Solomon,
1951; Whaley, 1978, Monsell, 1991), polysemy (Jastrembski, 1981), Concreteness-
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Abstractness, Imageability (Paivio, Yuille & Madigan, 1968), Age of Acquisition
(Gilhooly, 1984), Ease of Predication (Jones, 1985), and Contextual Distinctiveness
(McDonald, 2000a; McDonald & Shillcock, 2001). The goal in each case has been to
demonstrate that the variable in question can account for a unique and significant part of
the variance in participants’ performance in lexical processing tasks, and to provide
independent motivation for that variable. Word frequency is the paradigm example:
typically it accounts for a substantial fraction of the variance in tasks such as visual
lexical decision or naming, and can be motivated by the Hebbian notion that a
frequently activated representation becomes progressively more securely stored and
easily activated (Monsell, 1991). However, word frequency is correlated with the age at
which an individual tends to acquire a particular word, and several researchers have
proposed that Age of Acquisition is the true cause of the effects previously attributed to
word frequency (Brown & Watson, 1987; Morrison, Ellis & Quinlan, 1992). Many of
the lexical variables listed above are similarly intercorrelated, often very highly, and
there are substantial problems in experimentally disconfounding the roles they may play
in processing (Morris, 1981). The way that these variables interrelate remains a subject
of continuing research.
This brief and partial sketch of research on the mental lexicon has raised a number of
themes that bear on our current study. We will explore how very different words might
be related in a model that assumes that the whole lexicon may influence the processing
of any one word. We will attempt to go beyond the concern with either phonological or
semantic representations by considering the relationship between these two kinds of
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representation. We will compare this way of prioritising words in the lexicon with more
extensively investigated variables such as word frequency.
Assumptions
We make a number of critical assumptions, which we discuss in turn, below.
(a) The lexicon changes to accommodate cognitive constraints. The lexicon of any
human language is constantly changing, reflecting the fact that describing the world is
an open problem, always demanding new referring expressions. Some part of language
change will be conditioned by the processing demands of the brain. Words must be
capable of being easily learned, stored and accessed for speech recognition and
production. It follows that we should be able to detect, to whatever small extent, the
processing signature of the brain in the statistical structure of the lexicon. If we know
the processing preferences of the brain, then we should find some reflection of those
preferences in the structure of the lexicon of an individual language like English.
(b) The brain prefers systematicity in representations. Topographic mappings are
pervasive in
mammalian brains. They preserve structure between representations at
different levels and in different locations. In the human brain we see numerous
topographic maps in the visual system, for instance, in which the spatial relations of the
image falling on the retina are preserved in successive sub-domains of visual
processing. There are several different claims regarding the adaptiveness of topographic
mappings (see, e.g., Knudsen, du Lac & Esterly, 1987), but it is clear that the
systematicity of such mappings is a powerful representational principle, facilitating
learning, categorisation and generalisation. If structural relations within and between
real-world stimuli are preserved in the representation of those stimuli in the brain, then
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the brain retains the option of performing further, analogically based inferences on
various aspects of those stimuli: a systematic representation brings with it a lot of
inference “for free”. Because the brain often recycles the solutions to evolutionarily
simpler problems, we might expect such a principle to be in evidence at all levels of
representation in language processing; indeed, in the auditory cortex there is tonotopic
mapping from the cochlea onwards (Mazza, de Pinho & Roque, 1999), and in other
brain areas words belonging to different semantic categories cause location-specific
activity (Perani, Schnur, Tettamanti, Gorno-Tempini, et al., 1999).
If we extend this principle to the contents of the lexicon, then we might expect a
systematic relationship between phonological form and meaning: meaning should be
derivable from form, to some extent, and form from meaning – similar sounding words
should tend to have similar meanings. Thus, if we were designing the ideal lexicon of a
natural language we might begin by thinking, for instance, that all of the words for
edible things should start with /tß/, all of the words for pleasant things should contain
/ˆ/, all of the words for deep-fried things should contain the segment /p/, and so on.
Such a systematic lexicon would be maximally easy to learn and to organise for lexical
access, and novel words could be coined by speakers and interpreted by listeners using
transparent analogy with existing words. (For some of the early interest in this notion,
see Wilkins, 1668.) However, there are clearly severe limits to such a project. Speakers
and listeners need to refer to tens of thousands of different entities, using words that are
not prohibitively long. A compromise might be to save systematicity just for some
important core of the lexicon, and allow the principle to be replaced by different criteria
for the rest of the words. We might, for instance, find systematicity in the monosyllabic,
monomorphemic words or perhaps in a subset of these words, such as the words that
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need to be learned first, or the most communicatively salient words. In contrast, the
polymorphemic words have a thoroughgoing, explicit compositional structure: they are
constructed from two or more morphemes, which contribute to the overall meaning in a
predominantly predictable way and adult speakers of the language are aware of many of
the apparent rules.
In sum, we might expect to find some relationship between phonological form
and meaning, albeit a small one. Indeed, it might be surprising if the lexicon had
resisted such an apparently fundamental organising principle of the brain.
(c) Superpositional nature of lexical representation. There are many ways of theorising
about the lexicon and the nature of lexical representation. In symbolic computational
terms we can model words as single addresses in some system, so that the activation of
any one word is relatively unaffected by the presence of the rest of the words in the
lexicon. For instance, in Forster’s (1976) search model of lexical access the address of a
word is reached more or less quickly depending on the number of intervening addresses,
but once reached its activation is independent of the state of the rest of the lexical
representations. Alternatively, the level of activation of any one lexical representation
might be directly affected by that of one or more other words; for instance, in the
Interactive-Activation Model of visual word recognition (Rumelhart & McClelland,
1981; McClelland & Rumelhart, 1981), and in the related model of spoken word
recognition TRACE (McClelland & Elman, 1986), there are mutually inhibitory
connections between word nodes. At the other extreme of the modelling spectrum from
symbolic or localist models are models in which words are represented in a distributed
fashion, and in which superpositional storage ensures that the activation of any one
lexical representation is intimately influenced by the state of all the others that the
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architecture sustains. In a model like that of Seidenberg and McClelland (1989), this
relationship between one word and every other word is opaque, being expressed in the
hundreds of weighted connections that mediate the mapping between orthography and
phonology (and semantics in later developments of this type of model). We will assume
that the models towards the distributed end of the spectrum are more psychologically
realistic, and that there are constraints of some kind on the parallel activation of more
than one word; that is, the activation of any one word is contingent to some degree on
the state of all of the rest of the words in the lexicon. This assumption reflects the nature
of the computational models of lexical processing which currently provide the best
coverage of the human data, but it should be noted that each word could still be
influenced by every other lexical representation without assuming superpositional
storage.
(d) The relationship between form and meaning. Although we have cited Saussure’s
claim that signs are arbitrary, ever since Plato there have been arguments for a departure
from such an arbitrary relation between form and meaning within individual languages.
These claims take the form of anecdotal or descriptive accounts of “sound symbolism”.
Thus, for instance, Ultan (1978) has claimed that there is a relation between vowels and
the semantics of size. Following work by Tsuru and Fries (1933), there have been a
number of studies demonstrating that subjects with no knowledge of a particular
language (Japanese, in the initial studies) can still guess the polarity of antonyms such
as “hot” and “cold” in that language at a level greater than chance. Finally, there are
etymological claims that semantically and phonologically related clusters of English
words, such as glint, gleam, glimmer, glisten, glister, glow, glare, glance, gloom,
gloaming and hump, lump, slump, bump, plump, rump, dump, clump owe their
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existence to common origins in a putative proto-Indo-European language and
subsequent traffic between related languages. Thus there are apparent departures from
an arbitrary relation between form and meaning in particular languages, but no
comprehensive quantitative exploration of the phenomenon and no demonstration that
such observations are even statistically significant in the context of a large sample of the
lexicon of a particular language.
In summary, we hypothesise that there will be a significant relationship between
phonological form and meaning. To some limited extent, words that sound similar
should tend to have similar meanings. For any two words, the phonological distance
between them should tend to be related to the semantic distance between them. Thus, if
we can calculate these two types of distance, this Relatedness of Form and Meaning
(RFM) Hypothesis states “There should be a significant correlation overall between all
of the phonological distances and the semantic distances between the words in the
lexicon, even when explicit compositional morphology and sound symbolism are
excluded”. A corollary of this hypothesis is that such systematicity should be adaptive,
that it should be possible to find processing advantages that facilitate communication
between speakers and listeners. We now turn to the precise calculation of the
phonological and semantic distances in the studies we describe below.
Measuring phonological distance
We calculated the phonological distances between words as edit distances – the number
of changes necessary to turn one word into another, lead into gold for instance. The
phonological representations of words were taken from the Festival Speech Synthesis
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System2 and were based on distinctive features. Festival uses eight features: one
distinguishes consonants from vowels; four classify vowels by length, height, frontness
and lip rounding; three classify consonants by type (taking the values of stop, fricative,
affricate, nasal, lateral and approximant), voicing and place of articulation. Using these
features we created a mismatch function assigning a penalty to each pair of two phones.
Vowel features, such as length, are naturally scalar. For these we assigned a penalty of 1
for a small mismatch, and a penalty of 2 for a large mismatch. Consonant features, such
as voicing, were treated as nominal variables: all mismatches attracted a uniform
penalty of 1 on each dimension. Pairs involving one consonant and one vowel were
assigned an additional penalty of 10. This penalty schedule was created on the basis of
phonetic knowledge alone, without considering specific words and without knowledge
of the semantic distance between any pair of words. It was never varied in the studies
reported below after its initial creation. The procedure produced a set of phone-phone
distances. Distances between words were generated by applying the Wagner-Fischer
edit distance algorithm (Wagner & Fischer, 1974), using the penalty schedule described
above, augmented with a uniform penalty of 5 for deletions and insertions.
We adopted this simple metric of phonological distance because it is relatively
transparent. To illustrate some of the implications of the measure we took a random
sample of 40 monomorphemic, monosyllabic, four-phone words and created the tree
structure shown in Figure 1, in which the adjacent “leaves” of the tree are the
phonologically most closely related words in the sample. In the set of 1733
monosyllabic, monomorphemic words used in the main study below, the two words
with the greatest phonological distance between them were lea and prompt.
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Thus, we created a measure that allowed us to specify the phonological distance
between any two words. The measure is one that is motivated by speech science and
does not incorporate any hidden assumptions from detailed psycholinguistic theories
about how words might be stored and accessed. The fact that the basic phonological
representations were taken from a state-of-the-art speech synthesis system is evidence
that they are sufficient for the recognition of spoken words drawn from a large
vocabulary. The edit distance algorithm was already in existence and was not developed
with current issues in mind. The phonological edit distances generated were thus
independently motivated and constitute a conservative test of the RFM Hypothesis.
Measuring semantic distance
With the availability of large electronic corpora of text and transcribed speech, together
with fast computers, it has been possible in recent years to characterise words by the
contexts in which they occur in very large volumes of language (Lund & Burgess, 1996;
McDonald & Shillcock 2001; McDonald, 2000a). These syntagmatic, distributional
definitions of words have been equated with their meanings for the purpose of
modelling psychological data (Landauer & Dumais, 1997; Lund, Burgess & Atchley,
1995; McDonald, 2000a,b; McDonald & Shillcock 2001), echoing philosophically
based arguments that word meaning should be defined by usage (Wittgenstein, 1958;
Cruse, 1986). Several researchers have shown that distances within such highdimensional spaces can accurately predict a variety of semantic priming data observed
with human subjects (McDonald & Lowe, 1998; Lund, Burgess & Atchley, 1995;
McDonald & Brew, submitted; Monaghan, Shillcock & McDonald, submitted); perhaps
counter-intuitively, these context-based measures can also predict visual lexical decision
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times for words presented in isolation (McDonald & Shillcock, 2001). Throughout the
current work we will refer to the distances measured in the high-dimensional space we
construct as “semantic” or “meaning” distances, with the implicit understanding that
there are aspects of word meaning, as construed by linguists, philosophers and normal
adult speakers of the language, that such an approach cannot capture. For instance, such
a semantic definition of chair cannot directly capture what a chair looks like, or what it
feels like to sit in a chair. Furthermore, although we do not employ detailed
configurational information in the construction of the semantic distances, they will
nevertheless also embody syntactic information3.
The best alternative to such a context-based definition of word meaning has been
a feature-based approach in which, for instance, human subjects generate collections of
features for each word (see, e.g., MacRae, de Sa & Seidenberg, 1993, 1997), or the
modeller stipulates a range of features for the classification of each word (see, e.g.,
Plaut & Shallice, 1993). Feature-based approaches involve arbitrary assumptions about
the number and identity of the features, quite apart from raising more fundamental
philosophical questions about their adequacy. The corpus-based calculations we
describe below provided us with semantic distances between each word and every other
word. A feature-based approach would require human judgements to be made on a
sufficient number of features to capture something of the meaning of all 1733 words we
use in our main study, even the function words like of and the, and yet still represent
differences between closely comparable words such as chair and stool. For a set of 1733
words, this task would be an unfeasibly large undertaking. An alternative to the hand
coding of semantic features is to employ a semantic distance measure based on an
existing electronic dictionary (see, e.g., Harm & Seidenberg, 2001, submitted); however
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such an approach still involves the subjective judgements of the lexicographers, and in
that respect is inferior to the objective corpus-based approach we have used.
Furthermore, such an approach typically cannot code function words and content words
in the same terms. In the procedure we adopted, the same criteria are applied to all
words and any differences between word types are emergent differences, as opposed to
having been stipulated in the initial representations.
We constructed a high-dimensional space from the distributional information
contained in the 100 million words of contemporary English comprising the British
National Corpus (BNC)4 (Burnard, 1995). Within the corpus we replaced inflected
words by the corresponding lemma (e.g. walks was replaced by walk). Lemmatisation
preserves the meaning shared by the inflectional variants of a word yet reduces the
sparseness of the distributional data. To create a context vector representation for a
given lemma, we recorded the frequency with which each of 500 “context” words
occurred within a window of five words before and five words after each token of the
lemma. Thus, each word type was represented by a 500-dimensional context vector.
However, the reliability of such vector representations diminishes with word frequency
(McDonald, 2000b); hence we computed context vectors for only the 8000 most
frequent words in the BNC. We defined the semantic distance between any two words
in the resulting high-dimensional semantic space as (1 - cosine of the angle between the
vectors). Word pairs that have similar distributional properties (i.e. they each occur to a
similar extent with the same set of words) received a low semantic distance score,
whereas the semantic distance was larger between words that have divergent
distributional properties. Figure 2 shows the random sample of 40 words represented in
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Figure 1, but this time mapped according to their semantic distances from each other to
show the intuitive plausibility of the measure.
In summary, we calculated the semantic distance between every possible pair of
words. This approach to measuring semantic distance was independently motivated:
closely similar measures have been successfully used to model a range of language
processing behaviours. Further, explorations of the various parameters used in such
modelling (e.g. number of context words, size of the context window, type of distance
measure) have shown that the approach is robust (Levy & Bullinaria, 2001); modifying
the basic parameters typically has only small, predictable, quantitative consequences.
The same semantic distance metric – developed prior to the current studies, and without
recourse to the phonological descriptions of the words – was used throughout the
studies described below.
The relationship between semantic and phonological distance
We tested the RFM Hypothesis, which predicts that we should find a significant
correlation between phonological form and meaning even in the simplest words (know,
work, man, …) that intuitively seem to embody the claim that the relationship between
form and meaning is arbitrary. Our two distance measures were independently
motivated and independently developed, and thus constitute a conservative test of the
hypothesis.
We excluded complex words (e.g., dis-claim, steer-age) from the test set
(lemmatisation only affected inflectional variants); such words have an explicit,
compositional structure and, despite semantic drift between derivationally related words
(as in steer and steerage, for instance) it would not be surprising to find these words
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collaborating in a relationship between form and meaning. However, even words judged
to be monomorphemic by current intuitions might still have morphologically complex
origins that are implicit in their form. Thus circle and circuit are currently perceived to
be monomorphemic, in that they do not contain morphemes that are productive in the
contemporary lexicon of English, but it would be surprising if there were no historical
relationship at all between them. Similarly, a quasi-morphological relationship between
meaning and form may also exist in the proto Indo-European clusters that we have
referred to above and which have monomorphemic, monosyllabic members: e.g., the
“pejorative” group slime, slut, slug, slag, slut, slush or the “nasal” group snore, sneeze,
snot, snort, snout, sniff. In addition, within the function words there are groups that
seem to constitute small paradigms, such as who, when, where, whom, which, what, or
could, would, should. It is not possible to control for the histories of all the words we
wished to test – reliable histories are not known for most of them – and once we have
excluded currently productive morphology it is not possible to say which lexical
regularities may or may not be a legitimate part of the phenomenon we wish to explore.
We opted for an initial compromise: we excluded all the polysyllabic words, on the
grounds that – like the circle and circuit example – they are perhaps more likely than
monosyllabic words to have morphologically complex histories that are of less interest
in our current study. (However, we shall return below to the relationship between form
and meaning for longer, complex words.)
We chose the monosyllabic, monomorphemic words in the CELEX lexical
database (Baayen, Pipenbrock & Gulikers, 1995), using that database’s criteria for
classifying the words. We restricted the generation of semantic representations from the
BNC to the 8000 most frequent words, so as to avoid the sparse data problem attendant
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on the less frequent words. Of this set, only 1733 were both monosyllabic and
monomorphemic. Although this sample is only a fraction of an adult speaker’s lexicon,
it still accounts for 63% of word tokens in the transcribed speech of the BNC – a very
substantial proportion of the English in normal use.
We tested the RFM Hypothesis: there should be a relationship between the
phonological and the semantic distances between each word and every other word in the
1733-word set. This use of all of the available data is a critical aspect of our analysis.
Previous studies of the structure of the mental lexicon have tended to restrict themselves
to the near neighbours of words. This restriction is largely motivated by the coarsegrained nature of the data available from reaction time experiments with human
subjects, in which it is not feasible to investigate interlexical relationships as delicate as
the ones we study in the current approach. Restricting the study of interlexical effects to
relations between neighbours that are one segment different from the critical word in
fact ignores 99.8% of the available data. By analysing all of the available data we
therefore see the structure of the mental lexicon from a new perspective – that of a
comprehensive, quantitative analysis.
The global relationship between form and meaning
We calculated Pearson's r correlations between the phonological and semantic distances
for each possible pair of words in the 1733-word sample – 1,500,778 pairs of distances.
This calculation generated a correlation of r = 0.061: semantically similar words tend to
be closer together in phonological space5. We have already seen that any systematicity
within the lexicon of a natural language can only be very small. The systematicity we
have revealed accounts for only a minute part of the total phonological and semantic
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variation in the 1733 words we have considered. However, we will demonstrate, below,
that further exploration of the lexicon yields measures of systematicity that are an order
of magnitude larger than the overall figure of r = 0.061, reported above. We will also
show that such measures of systematicity can account for significant amounts of the
variance in behavioural data. First, though, we tested the hypothesis that the overall
level of systematicity is statistically significant.
We used a randomisation test (Cohen, 1995). First, each word was randomly
assigned a single, unique partner in the set. Second, the correlation was calculated using
each word’s own semantic context vector and its partner’s phonological form, thereby
bijectively randomising the relationship between the two distances. This procedure was
repeated to generate the distribution, over 1000 iterations, of correlation coefficients
expected by chance. The veridical value of r was an outlier on this distribution (z = 4.2,
p < .001 (one-tailed)).
We have revealed a relationship between form and meaning in the
monomorphemic core of the English lexicon, as predicted in the RFM Hypothesis, and
we have quantified that relationship. We now consider other aspects of this overall
relationship between form and meaning. In Figure 3 the bar chart represents the
distribution of phonological distances within the 1733 words. This distribution is
predictably bell-shaped: there are relatively few very large or very small distances and
the modal phonological distance between two words is 12 feature-level edits, as defined
by the edit-distance algorithm. Consider only that subset of phonological distances in
which the two words were four edits apart. We plotted the distribution of the
corresponding semantic distances, that is, all the semantic distances between the pairs of
words in the four-edit subset. This distribution was also bell-shaped; there were
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relatively few very large or very small semantic distances. The mean of this set of
semantic distances was calculated, and is one of the points plotted in Figure 4.
Similarly, for all of the phonological distances of each length, the mean of the
corresponding semantic distances was calculated and plotted in Figure 4, which shows
in summary form the relationship between the phonological and semantic distances. The
relationship is strikingly linear, and all but monotonic across the range of phonological
distances, only becoming noisier at the extremes of the distribution where the data are
sparse. The more phonologically disparate two monosyllabic words are, the more
semantically disparate they tend to be, regardless of the absolute phonological distance
between them. Phonologically dissimilar words tend to be semantically dissimilar, but
even more phonologically dissimilar words tend to be even more semantically
dissimilar.
In summary, we have quantified the overall relationship between form and
meaning in a substantial part of contemporary spoken English. We have tested the
principal prediction of the RFM Hypothesis, and shown that even at a global level there
is a tendency towards coherence, in that greater phonological disparities between words
tend to be associated with greater semantic disparities. In the further analyses we
present below, we will analyse what lies beneath this overall relationship. We will begin
by partitioning the set of 1733 words into coherent subsets.
The relationship between form and meaning in a partitioned lexicon
We began to identify the origins of the correlation by partitioning the set of 1733 words
by the syntactic category and frequency information found in CELEX, and by word
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length. We then used the randomisation test described above to determine the
significance levels of the correlation within each subset of words.
Controlling for syntactic category excludes some of the effects of known
phonological cues to syntactic category (Kelly, 1996). The distinction between content
words and function words is perhaps the profoundest distinction in the English lexicon;
it has formal, physical and processing dimensions. We tested the hypothesis that the
content/function distinction might be responsible for the observed systematicity; there
are typical phonological and semantic differences between content words and function
words, and we might therefore predict a significant correlation when such measures
span the category distinction. However, there should not necessarily be a significant
correlation between form and meaning within the two categories.
We used CELEX’s noun category to designate a large subset (1509 words) of
content words; note that a proportion of these nouns can occur as other syntactic
categories too. In contrast, there were 99 word types designated by CELEX as function
words. Table 1 shows that the correlation is significant within both categories. It is clear
that the function/content difference is not solely responsible for the overall formmeaning correlation.
The correlation found within the noun category shows that a relationship
between form and meaning exists in that part of the English lexicon that might seem to
contain the most atomic entities – monosyllabic, monomorphemic nouns. The presence
of a significant correlation between form and meaning is perhaps less surprising in the
function word category, given that several subsets of function words are phonologically
relatively distinct: the word-initial voiced dental fricative /∂/, as in the or there, is
particular to function words; in addition the wh-initial words and the -ould words seem
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to constitute small paradigms. Against this apparent structure within the set of function
words, their relatively small number militates against a high correlation. Overall, the
correlation within the function words category is highly significant.
If syntactic category does not drive the form-meaning correlation, then
phonological length – also a salient difference between words – seems to be a good
candidate for the cause of the correlation. We tested the hypothesis that the correlation
was caused by length differences, in which case there should be no significant
correlation within subsets defined by phonological length. To control for a possible
interaction with syntactic category, we used only the large subset of nouns in the
analysis in terms of length.
As Table 2 shows, the correlation is predictably non-significant for nouns of
lengths 1 and 2. There are only a very small number of the former, and the degrees of
freedom for producing different words is very limited in the latter. However, the
significance levels increase for the correlations found within the nouns of length 3, 4
and 5. There was predictably no significant effect for the five words of length 6. There
is no evidence that phonological length is mainly responsible for the correlation
between form and meaning. The best interpretation of the data in Table 2 is that a
significant correlation exists within any subset of words of the same length that contains
sufficient numbers of words, and that longer words provide more scope for such a
correlation.
Word frequency is a further important difference between words; it is itself
associated with differences in length and with the function/content distinction, and it has
processing implications. Word frequency is intimately connected with any contextbased measure of meaning, in that the more frequently a word occurs in a corpus, the
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more opportunity it has to appear in different contexts. We tested the hypothesis that
word frequency would be a significant dimension in explaining the correlation between
form and meaning. When a frequency-ranked list was split at the median, the correlation
in each half was significant, but the effect was stronger in the less frequent set, as is
seen in Table 3. The data in Tables 2 and 3 may both reflect the fact that less frequent
words tend to be longer. Longer words may provide more scope for the correlation to
occur. Note that even though all the words in this study were monosyllabic, there were
differences in the complexity of the onset and the coda – as evidenced in the maximum
edit distance observed, that between lea and prompt.
We tested the frequency-based hypothesis more stringently by dividing the
words into narrower frequency bands. When the 1733 words were ranked according to
frequency and divided into eight 200-word sets from the top, the correlations between
form and meaning were as shown in Table 4. For the first time, we see a sporadic
structure in the appearance of the correlation between form and meaning. If the
relationship between form and meaning were adaptive, perhaps facilitating language
processing, then we would expect to find greater systematicity within the most frequent
words. These words are in a routine state of being activated and deactivated within short
periods of time in normal language use, and should have the most to gain from any
processing benefits. A failure to find systematicity in this important part of the lexicon
would embarrass any claim that lexical systematicity is adaptive. The significant
correlation within the set of the most frequent 200 words supports our claim that lexical
systematicity is adaptive, and is perhaps all the more remarkable in that we have already
seen, in Table 2, that the form-meaning correlation tends to be strongest in the longer
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words. Frequent words tend to be short, yet we see a highly significant correlation in the
most frequent subset.
The sporadic pattern of correlations shown in Table 4 indicates that word
frequency is potentially relevant to understanding the overall correlation between form
and meaning. In the frequency subsets below the most frequent one, we see a trend in
which the correlation becomes stronger for the less frequent words: of the three next
most frequent subsets (201–400, 401–600, 601–800) the first two are little different
from zero, and the third is in the predicted direction, whereas in the three least frequent
subsets (1001-1200, 1201–1400, 1401–1600), two are highly significant and the other is
in the predicted direction. This trend may be best interpreted in terms of the previous
observation that longer words tend to exhibit the correlation between form and meaning
more strongly.
The significant correlation within the set of the 200 most frequent words
suggested that the effect might be solely due to function words, which dominate the
high frequency range. We tested this claim by dividing only the nouns into frequency
sets. Table 5 shows the same profile of significance levels as Table 4, although the
levels are reduced in both of the more frequent sets. The systematicity observed in the
most frequent words was not due to the function words alone.
Given that this systematic relationship between form and meaning seems to be
pervasive in the lexicon of English, what can we say about its origins and the way in
which it is mediated? We tested the possibility that the observed systematicity was due
to the way in which we calculated the semantic distances. Because we defined the
meaning of a word in terms of the identity of the other words that appeared in close
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proximity to it, we may have also been implicitly recording phonological constraints on
which words are likely to occur with other words; for instance, there may well be a
phonetically-based predisposition against alliteration, so that tongue-twisters are
avoided. If pick were one of the 500 context words, we might perhaps expect it to occur
less in the context vector representations for peck, pit and pun, calculated over the
adjacent words; thus, the phonological similarity between peck, pit and pun might lead
to an apparent semantic similarity. This issue is not straightforward to resolve. It is not
possible simply to replace the context-vector approach to calculating semantic
distances, in favour of a measure that does not reflect nearby words. No other measure
of semantic distance provides the coverage and possesses all the advantages of the
context vector approach. It is possible to generate large-scale coverage of the words in
question by deriving a distance measure from WordNet (Miller, 1990) based on the
number of intervening nodes between two words, but this approach only produces
relatively coarse-grained semantic distances which ultimately reflect the judgements of
the lexicographer, and which are particularly inconsistent in the case of the function
words. It is possible to require human participants to make judgements of semantic
similarity, but this approach would only be feasible for a tiny proportion of the distances
required, and would often involve psychologically bizarre judgements. The best test of
the hypothesis that our reported form-meaning relationship was actually a form-form
relationship is to assume that phonological constraints are stronger for words that are
closer together, and to recalculate the semantic distances based on a context window
that excludes the words immediately adjacent to the word being defined. We carried out
this test, using a five-word window that began two words away from the word being
defined. The resulting overall correlation between form and meaning fell to 0.0323, but
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remained significant (p = .012). The fall in the size of the correlation is predicted by the
fact that a word’s immediate neighbours typically have the strongest semantic
relationships with that word. The survival of the correlation between form and meaning
is less easily explained as a purely phonological phenomenon for this version of the
semantic representations.
We carried out a further study to compare the size of the form-meaning
correlation for a set of 2397 polysyllabic words for which reliable semantic
representations were available. These words included derived forms and debatably
related words (circle and circuit), but not inflected variants. The overall correlation
increased to r = .0845. This increase was predicted because the polysyllabic words
embodied compositional relationships between productive or historically productive
morphemes. This increase is most straightforwardly attributable to the inclusion of
compositionally related words in the set: revise and return contain a clear form-meaning
correspondence, as do circle and circuit.
In summary, the correlation between form and meaning could be observed in relatively
small subsets of the set of 1733 words. The correlation seemed to be partly predictable
from the length and the frequency of the words, although the effect appeared at the high
and low ends of the word frequency spectrum, possibly reflecting the fact that
systematicity is adaptive, and that there is more scope for systematicity in longer words,
respectively. The correlation between form and meaning was not due solely to the
content/function distinction. Importantly, the correlation could not be explained by
putative phonological constraints operating over the four words immediately adjacent to
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the words when they appeared in a large corpus of English. Finally, the correlation
increased predictably when the study was extended to polysyllabic words.
Individual words and the rfm ranking
In order to understand further the nature of the overall correlation between form and
meaning, we analysed each word’s individual contribution to that correlation. For each
word we took the 1732 pairs of distances between that word and every other word and
calculated the correlation between the two types of distance. This procedure gave each
word its individual meaning-form correlation, rfm. The words were then ranked by their
rfm values. For 1732 pairs of data points, the value of Pearson's r required to reach the p
< .05 significance level was approximately .037, and that for the p <.01 level was .055
(1-tailed). Figure 5 shows rfm plotted against rank position. The largest value of rfm was
.189, for oh; the distribution passes 0, approximately at the word plea, and falls as low
as -.115, for frank. Appendices A and B contain the first and last 50 words of this
ranked list, together with the value of rfm for each word. As the graph illustrates, some
798 words have a significant (p < .01) positive value of rfm, and some 62 words have a
significant (p < .01) negative value of rfm6. When the level of significance is dropped to
p < .05, these two numbers rise to 985 and 118, respectively. In summary, around half
of the words in our 1733-word sample possessed a significant positive value of rfm (we
return below to the issue of the negative values of rfm). The exact ranking we obtained
reflects the parameters chosen in defining the phonological and semantic distances;
changes to these parameters will produce some reordering of the rfm ranking. In line with
the rest of our study, we present an analysis of the single rfm ranking obtained with our
initial parameters; a more comprehensive investigation of the parameter space would
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probably reveal related rankings in which the behaviours of interest emerge even more
clearly than in the single ranking we present here.
Viewed from the perspective of the rest of the lexicon, we may think of the
positive rfm value of a particular word as the rest of the words in the lexicon conspiring
to reinforce the relationship between the phonological form and the meaning of the
word in question. For instance, the word we is eleventh in the ranked list; it therefore
has one of the strongest positive correlations between the phonological and semantic
distances from itself to the rest of the words in the lexicon. If either the form or the
meaning of we were discretely erased from the mental lexicon of an adult speaker of
English, then the one could be partially inferred from the other in conjunction with the
rest of the words in the lexicon. Thus a word, like we, with a large positive value of rfm
is relatively firmly embedded within the language. In contrast, the rest of the lexicon
provides no support for inferring form from meaning, or meaning from form, for the
word plea, which has a near zero value of rfm. The word friend has a significant negative
value of rfm, which we may interpret as the rest of the words in the lexicon tending to
constrain the meaning of friend, given the form, (or the form, given the meaning) to be
somewhat different to what is actually the case. That is, for the words at the bottom of
the ranking, there is pressure from the rest of the lexicon for phonological change or
semantic drift.
In summary, although the overall correlation between form and meaning in our
set of words was small and therefore accounted for only a tiny proportion of the total
variation in form and meaning, when we look at the distribution of the effect and at the
contribution of individual words, we see that the effect is not confined to the margins of
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the lexicon. Around half of the rfm values were significant, and the overall effect was
significant in qualitatively different partitions of the lexicon.
A cursory inspection of the top of the ranked list shows that certain categories of
words are prominent in that part of the list. We discuss some of the most prominent
categories prior to suggesting a characterization of the ranking. The distribution of these
four categories in the rfm ranking is shown in Figure 6.
Editing terms. Filled pauses and editing terms, used by speakers to interrupt or to hold
onto the turn, are prominent: oh (1), er (3), ah (5), eh (13), ah (44), hey (86). These
words are derived almost exclusively from the 10% of the BNC that consists of
transcribed speech. In many studies of lexical processing such items are marginalised as
paralinguistic “noise”, devoid of conventional semantic meaning. However, their
communicative importance in managing turn-taking has been acknowledged in other
studies (see, e.g., Clark, 1994, 1996).
Swear words. The paradigm English swear-word, fuck, appears 16th in the list – within
the top 1% – to be closely followed by the other high-affect swear words shit (18th
position) and bitch (25th position). Because of their affective force such words are
communicatively important. Van Lancker & Cummings (1999) present a
comprehensive review of research on swearing, in which the authors cite 25
monosyllabic, monomorphemic swear words. Only 16 of these words (with very minor
modifications to allow for comparison with a British English corpus) appeared in our set
of 1733 words, the remainder being too low frequency. Appendix C lists the words and
their values of rfm, showing that 12 of the 16 words (down to and including arse)
occurred in the top half of the ranking. Within the list of the 16 swear words, those with
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higher affect tend to appear closer to the top of the list, and those with frequent nonexpletive meanings tend to appear closer to the bottom of the list.
Personal pronouns. The personal pronouns cluster at the top of the rfm ranking, all but
one falling in the top decile: you (2), we (11), it (12), me (15), I (24), us (26), he (40),
she (43), they (82), him (114), them (167), her (641)7. Even the archaic pronouns ye(6)
and thou (126) pattern with the rest of the category. (Note, in this respect, that our
semantic distance measure effectively normalises for frequency; it is the degree to
which the context vector is populated that is important in the measure, rather than the
total number of occurrences of each context word.) It is instructive to compare the
relative positions of thou (126) and the (713) in the ranking. We might perhaps have
expected that the two words would both occur close together and high in the ranking
because they are phonologically similar function words, or we might have expected that
the very frequent the would be higher in the ranking than the less frequent thou. In fact,
the occurs close to the middle of the ranking. Overall, any initial prediction that the
function/content distinction would determine the position of words in the ranking is not
borne out. Instead, the personal pronouns – a subset of the function words – are clearly
seen at the top of the rfm ranking.
Proper names. The unambiguous proper name mick appears in 7th position. Figure 6
shows the distribution of all of the words in the 1733-word set that are frequently used
as names, either in their own right (e.g. john) or as contractions of longer, generally
polysyllabic names (e.g. ken, tom). Many of these names are homophonous with simple
content words (e.g. nick, frank); we do not distinguish between the different meanings
of homophones in our analyses. The distribution of names is skewed towards the top of
the rfm ranking.
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In summary, this brief, qualitative, post hoc analysis has shown that in the upper half of
the rfm ranking a number of clearly defined categories of words stand out: editing terms,
swear words, personal pronouns and proper names. We have seen in the previous
analyses of subsets of the 1733 words that the correlation between form and meaning
exists in both the content words and the function words. The four subsets we have
identified bear out the fact that there is no simple content/function distinction between
the top of the ranking and the rest of it. Rather, the relevant distinction seems to involve
the pragmatic force of the words in question. The four sets of words share the property
of being pragmatically important: the editing terms help manage the conversational
interaction, the swear words add affect to the interaction, and the personal pronouns and
proper names both have pragmatically important referential functions. This observation
concerning the communicative importance of the four sets of words considered returns
us to the key question, which we presented as a corollary to the RFM Hypothesis: is the
form-meaning structure, revealed in the rfm ranking, an adaptive one? On the basis of
our informal, post hoc analysis of categories of words appearing near the top of the rfm
ranking, we provisionally conclude that there is circumstantial evidence that the formmeaning correlation is adaptive. A word near the top of the list accrues the support of
the rest of the lexicon in occupying a stable form-meaning position. It is adaptive, in
terms of acquiring and processing spoken words, for communicatively important words
to occupy such a position. This interpretation would be falsified by a language in which
these sorts of words possessed a phonological form that placed them in the lower
reaches of the rfm ranking; for instance, a version of English in which these
pragmatically important words possessed elaborate onsets, nuclei and codas.
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There are severe limits to such a post hoc approach to testing the claim that the
form-meaning correlation is adaptive. We therefore turn to the relationship between the
form-meaning correlation and a number of clearly quantifiable variables that researchers
have associated with lexical processing. We will then return to the question of whether
or not the correlation is adaptive.
The rfm ranking and other lexical variables
An informal comparison of the first and the last 50 words in the ranked list suggests a
number of factors, other than pragmatic or communicative importance, that might be
implicated in the ordering of the words: for instance, word frequency and length appear
to differ markedly between the two ends of the ranking. We analysed the relationship
between rfm and the following widely studied lexical variables taken from the MRC
Psycholinguistic Database (Coltheart, 1981): age of acquisition (AOA) (Gilhooly &
Logie, 1980), spoken word frequency (BFREQ) (Brown, 1984), concreteness (CONC)
(Gilhooly & Logie, 1980; Pavio, Yuille & Madigan, 1968; Toglia & Battig, 1978),
written word frequency (overall frequency (KFFREQ), “number of samples” (KFSMP),
“number of categories” (KFCAT)) (Kucera & Francis 1967), familiarity (FAM) and
imageability (IMAG) (Pavio, unpublished; Gilhooly & Logie, 1980; Toglia & Battig,
1978), number of letters (NLET), meaningfulness (CMEAN) (Colorado) (Toglia &
Battig, 1978), meaningfulness (PMEAN) (Paivio, unpublished), number of phonemes
(NPHN), and written word frequency (TLFREQ) (Thorndike & Lorge, 1944).
Word length. There was a robust correlation between rfm and word length: NLET (r = .455, p < .001, N = 1732) and NPHN (r = -.522, p < .001, N = 1666). Words with a
larger, positive value of rfm tended to be shorter.
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Word frequency. There was a further robust correlation between rfm and the various
measures of frequency, with the exception of KFCAT: BFREQ (r = .189, p < .001, N =
1132), KFSMP (r = .175, p < .001, N = 1685), KFFREQ (r = .081, p <.001) and
TLFREQ (r = .121, p < .001, N = 1650)8. Words with a larger, positive value of rfm
tended to be more frequent.
In summary, objective measures of word length and frequency correlate strongly
with rfm in the directions visible in Appendices A and B, although only word length
accounted for a substantial part of the variance. We now report the relationships with
the various subjectively rated lexical dimensions.
Age of acquisition. AOA emerged as the subjective variable with the strongest
correlation with rfm (r = -257, p < .001, N = 466). Words with a larger, positive value of
rfm tended to be those words that people judge to be acquired earlier in life.
Concreteness. Concreteness was significantly correlated with rfm: CONC (r = -.066, p <
.012, N = 1180). Words with a larger, positive value of rfm tended to be less concrete.
Familiarity. Familiarity correlated significantly with rfm: FAM (r = .166, p < .001, N =
1215). Words with a larger, positive value of rfm tended to be words that are judged to be
more familiar.
Imagability. Imagability correlated significantly with rfm: IMAG (r = -.079, p < .003, N
= 1209). Words with a larger, positive value of rfm tended to be less imagable.
Meaningfulness. Meaningfulness was correlated significantly with rfm, but only
marginally so for one of the two measures: CMEAN (r = -.092, p < .002, N =993) and
PMEAN (r = .105, p = .093, N = 159). Words with a larger, positive value of rfm tended
to be less meaningful.
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In summary, the subjectively rated variables all correlated with rfm. The rfm
measure is objectively determined and succeeds in capturing some of the variance from
the range of subjective variables. In order to provide a further basis for discussing the
relationship between rfm and the other variables, we assessed how well rfm predicted
published data from large-scale studies of visual lexical decision (Balota, Cortese &
Pilotti, 1999) and word naming (Balota & Spieler, 1998; Spieler & Balota, 1997).
Modelling word naming. We tested the extent to which rfm captured the naming data
from groups of young (YOUNGNAMRT) and older (OLDNAMRT) participants, as
reported by Balota and Spieler. Naming time was significantly correlated with rfm for
both groups: YOUNGNAMRT (r = -.205, p <.001, N = 1589) and OLDNAMRT (r = .142, p < .001, N = 1589). The higher in the rfm ranking, the faster the word was named.
Across the board, the effects were typically smaller for the older participants compared
with the younger ones, although the patterns of significance were similar; consequently,
we only report the analyses of the data from the younger participants.
Of the other variables considered, the objective variables of length, NLET (r =
.365, p < .001, N = 1589) and number of segments, NPHN (r = .265, p < .001, N =
1545) were good predictors of naming time. Partialling out NHPHN reduced the
correlation between rfm and YOUNGNAMRT, but showed that rfm still accounted for a
unique part of the variance (r = -.091, p < .001, N = 1542). Several of the word
frequency measures were also predictors: BFREQ (r = -.065, p < .018, N = 1047),
KFCAT (r = -.184, p < .001, N = 1577), and KFSMP (r = -.147, p < .001, N = 1577).
Partialling out KFCAT did not affect the portion of the variance accounted for by rfm (r
= -.210, p < .001, N = 1574); partialling out rfm showed that KFCAT and rfm accounted
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36
for independent parts of the variance. In contrast, the spoken word frequency variable
BFREQ did not account for a significant part of the variance independent of rfm:
Of the subjective variables, AOA was the best predictor of naming time (r =
.286, p < .001, N = 447), followed only by FAM (r = -.224, p < .001, N = 1155). Partial
correlations showed that neither of these variables subsumed rfm, which continued to be
significantly correlated with naming time when AOA and FAM were each partialled out
(r = -.188, p < .001, N = 444; r = -.195, p < .001, N = 1152, respectively).
Modelling visual lexical decision. The rfm variable’s robust role in accounting for
naming time variance contrasted with its relationship with the lexical decision data. The
rfm variable did not correlate significantly with either the data for the young or the older
participants reported by Balota et al. (1999) (r = -.010, p = .342, N = 1598; r = .008, p =
.382, N = 1599, respectively). Other variables tested, such as AOA (r = .411, p < .001,
N = 451), KFCAT (r = -.391, p < .001, N = 1584) and FAM (r = -.462, p < .001, N =
1162), showed significant correlations.
Discussion
We have shown that the correlation between form and meaning in the English lexicon
can be seen as a dimension of lexical variation that can account for reaction time data in
the naming task. A substantial part of the psycholinguistic literature is devoted to the
competing claims made for dimensions such as word frequency and age of acquisition,
in support of different processing accounts of lexical access. This issue is complicated
by methodological problems. As we have shown in the analyses we have reported of
naming time and lexical decision data, a number of different variables correlate
significantly with the observed behavioural data. In principle, their independent
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contributions can be shown using factorial experimental designs or linear regression
analyses. In practice, it is often problematic to fill all of the cells of a factorial design
with plausible stimulus material; for instance, in comparing age of acquisition and word
frequency, it is not straightforward to find low frequency words that are acquired early,
and high frequency words that are acquired late. De Jong (2002) has argued, on the
basis of Monte Carlo simulations, that regression studies are likely to be more
successful than factorial experiments in resolving this issue. However, regression
studies encounter the problem that the independent variables being compared may
themselves be highly intercorrelated; in the studies we have reported here, age of
acquisition correlated -.420 with concreteness (CONC), -.641 with familiarity, and -.529
with imagability, for instance.
One response to this dilemma is to prioritise the different variables according to
a priori theoretical principles. We suggest four such principles, below, and discuss each
in turn.
(a) Objectivity. We can draw a distinction between variables derived from objective
measurements, such as word frequency, and those that are subjective evaluations, such
as familiarity and age of acquisition. The experimenter has little control over the factors
that contribute to the relatively non-intuitive judgement of, for instance, the age at
which a particular word might have been acquired. Although subjective judgements of
age of acquisition behave in a similar way to objective observations of when words
enter children’s vocabularies, in predicting object naming speed (Ellis & Morrison,
1998), there is a danger that ill-defined factors such as the perceived importance of a
word in the culture might complicate the measure, while simultaneously allowing it to
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account for more reaction-time variance. The rfm variable is defined objectively, and
may therefore be more transparently incorporated into a theory of lexical processing.
(b) Generality. Some of the proposed predictors of lexical processing behaviour are less
general than others. Thus, the concept of “meaningfulness” is potentially problematic to
apply equally to the words table, being, and and. The principal division within the
lexicon is that between function words and content words. Although there are deep
distinctions between these two classes of word, both in formal linguistic analyses and in
psycholinguistic terms (see Cann (2000), for a review), these two types of word are
intimately blended in spoken language and the default assumption must be that there is
the maximal possible shared processing before the necessary divergence occurs between
the two classes. Priority in theorising should go to a variable that accurately predicts
processing behaviour in all categories of words, compared with a variable that predicts
processing in only one category. The rfm variable is attractive in that a value can be
generated for any word – from transcribed fillers like oh and er to concrete nouns like
priest or clerk – in exactly the same way.
(c) Integral nature of language. Just as it is preferable for a theoretical construct in a
theory of lexical processing to be able to refer, across the board, to all categories of
word, it is also desirable for such a construct to embody the fact that human language
has both semantic and phonological dimensions. The explanatory value of, for instance,
the dimension of concreteness is necessarily limited to particular aspects of semantic
processing and representation. Similarly, the dimension of word length is limited to
those aspects of spoken language processing and representation in which it is
appropriate to talk about the number of segments that a word contains. The dimension
of age of acquisition is relevant to issues of access and storage. In contrast, the rfm
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39
variable expresses the degree to which a particular word participates in the overall
systematicity that characterises the mental lexicon. Phonological descriptions and
semantic descriptions of words abstract away from the function of language, which is to
allow speech to cause meaning to be shared by the interlocutors. The rfm dimension
represents the reality of the relationship between form and meaning rather than the
abstract ends of that relationship. As such, it should be accorded theoretical priority
over variables that do not reflect the full domain of language processing.
(d) Pre-eminence of speech. Human language emerged exclusively as a spoken
phenomenon; widespread literacy is a recent cultural phenomenon, and even for most
highly literate individuals the mental lexicon is more frequently accessed by speech than
by text. People learn to speak and listen before they learn to read and write, and the
latter activities can be expected to be parasitic on the former in a variety of ways.
Accordingly, we should expect the exigencies of speech processing to have a central
role in a model of language processing. The rfm variable predicted behaviour in the
naming task, but not in the lexical decision task. This clear interaction by task is an
important result, as it separates the rfm variable from the other predictors of lexical
processing speed that we investigated, and situates the effective domain of the rfm
variable in the spoken modality. It supports our claim that the rfm variable embodies the
phonological and semantic role of a particular word within the whole of the lexicon, and
is not reducible to other variables such as age of acquisition. Differences between
lexical decision and naming predictors are typically interpreted, in the literature, as
reflecting the fact that a correct lexical decision can sometimes be based on orthotactic
criteria, whereas a correct naming response frequently requires the participant to have
uniquely identified the word and to have individuated its phonological form. Despite the
Mental Lexicon
40
fact that recognizing a visually presented word may well activate its phonology, there is
clearly a less central role for phonology in visual lexical decision than in naming.
In summary, we have explored the relationship between rfm and several of the variables
that have been proposed in the psycholinguistic literature as dimensions along which
lexical processing might be organised. They are correlated with rfm to a greater or lesser
extent, and in relatively predictable directions, but the results have enabled us to argue
for the rfm dimension as an important psycholinguistic variable in its own right.
rfm and the neuropathologies of language
The words at the top of the rfm ranked list resemble the output often observed in severely
impaired dysphasics. Van Lancker and Cummings (1999) reproduce the results of a
five-minute interview by N. Geshwind of EC, a right-handed adult who underwent a
left-hemispherectomy. He generated practically no propositional speech, spontaneously
producing the words one, three, I, no place, well, as, together with what Lancker and
Cummings refer to as “pause fillers” – un, boy, well, uh, no, eh, ah, nah, um, mm, oh
yes – and copious swearing in the form of the words goddammit and shit. We propose a
parsimonious processing-based explanation of such behaviour, in which the lexical
entries in the dysphasic brain are compromised across the board. This general
decrement in lexical processing reflects an overall hypoactivation in one or both
hemispheres, due, for instance, to cortical hypoperfusion (see, e.g., Hillis, Wityk,
Barker, Beauchamp, Gailloud, Murphy, Cooper & Metter, 2002). This assumption is a
minimal one regarding the physical location of impairment in the damaged brain. In this
state, with all of the lexical entries functioning sub-optimally, it is those entries that
Mental Lexicon
41
receive the greatest support from the rest of the lexicon that are able to achieve enough
coherence between form and meaning to be used. Thus, particular words do not
necessarily survive or become lost to use because of their physical location in the brain,
or because of their connection to particular processing pathways. Rather, the whole
lexicon is compromised, to whatever degree, and it is the sum of intra-lexical
relationships that determines the relative sparing of particular words. It is precisely the
editing terms – oh, er, eh – and other pragmatically important words that appear to
survive because their form-meaning coherence is reinforced by every other word’s form
and meaning, even though these other lexical entries may themselves be compromised.
A partial example of such survival has been reported by Shillcock and Hackett (1998),
who showed that non-fluent dysphasics retain the capacity to exploit the generalization
that only function words have word-initial /∂/ in English (Campbell & Besner, 1981);
even though the participants in the experiment were impaired in a way that is classically
defined in terms of a compromised function word lexicon, they were still able to recruit
orthographic and phonological information, in concert with the meaning of a sentential
context, to pronounce a nonword, like thap, in the same way as matched normal
participants.
We claim that the active vocabulary following damage tends to be reduced so as
to favour the words that fall at the very top of the rfm ranked list. The other words’ forms
and meanings may survive in a compromised state that does not reach the required level
of coherence for them to be reliably accessed, produced, or deployed.
Coprolalia – copious swearing – occurs widely in the various neuropathologies
of language. Frequently the swear words are among the earliest words to return to fluent
production after damage of one sort or another to the language processor, and they may
Mental Lexicon
42
continue to be used more prolifically after damage than before. A substantial proportion
of individuals presenting with Tourette’s syndrome over-produce swear words, often
with poorly produced articulation and prosody, in the context of normal speech. Van
Lancker and Cummings (1999) review the relevant data and theories of coprolalia in
both dysphasia and Tourette’s syndrome, concluding that an account of swearing in
both conditions might involve the dysfunction of the limbic system, which – among
other functions – mediates certain emotional behaviours and activities. They agree with
Nespoulous and Lecours (1987) in this account of coprolalia. Thus, they claim, in
Tourette’s syndrome there is a hyperactive production of what Van Lancker and
Cummings call “the overlearned and emotive vocal-motor “gestures”” (p. 99) of
swearing, and in aphasia the individual still has access to the vocalisations mediated by
the limbic system (and possibly also facilitated by the right hemisphere of the brain).
We suggest an independent, processing-based contribution to a theoretical
account of coprolalia, in which the swear words have the same status as the other
pragmatically important words that are preserved in the neuropathologies of language.
The swear words fuck and shit appeared in 16th and 18th positions, respectively, in the
1733-word rfm ranking. Van Lancker and Cummings cite these two words as figuring in
both aphasic and Tourette’s syndrome coprolalia; they also state that in other languages
the equivalents of these two words appear in all groups. Thus, the paradigmatic swear
words involved in neuropathologies of language appear at the top of the rfm list. These
and other swear words at the top of the rfm ranking are made more salient by the rest of
the contents of the lexicon. In our account of coprolalia, a generalised activity (i.e. a
“tic”) in the lexicon could result in the swear words being pronounced, as a result of the
salience they possess in their form and meaning relationships with all of the rest of the
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43
lexicon. It is important to note that it is not just swear words that individuals with
Tourette’s syndrome produce; indeed coprolalia occurs in some 25–50% of Tourette’s
subjects. Fernando (1976) reports a case of an English Tourette’s subject with French
parents who produced maman. Note the ma and mum occur in our ranked list in
positions 8 and 23 respectively. Thus, we claim that it is a more parsimonious account
of the vocal tics that characterise Tourette’s syndrome to say that they are pragmatically
important words that have high salience in the systematic structure of the lexicon, rather
than to concentrate on the coprolaic nature of a minority of vocal tics.
In summary, the words produced by individuals with pathologically impaired
language may be characterised in terms of the correlation between form and meaning.
This conservative, processing account is based on the emergent behaviour of the
compromised lexicon; it is more inclusive than existing theories that are restricted to
coprolalia, or that ignore the broader pragmatic nature of the words that are most freely
produced, although it may naturally co-exist with such accounts.
rfm and proto Indo-European
What is the status of the Proto-Indo-European clusters of words, such as that containing
street, strip, stream, stripe, strap and stroke, which might be taken to share aspects of the
meaning “long, thin”? Intuitively these words seem to be the prime candidates for a
motivated connection between form and meaning in the lexicon. However, when we
inspect the rfm ranked list we find that far from being the main contributors to – or
beneficiaries of – the relationship between form and meaning in the lexicon, these
words are spread throughout the ranking and tend to appear more in the lower half of
the list. Figures 7 and 8 show histograms in which the str- and –ump (implying “curved,
Mental Lexicon
44
semi-circular shape”) clusters respectively are identified by decile. These two groups
are perhaps the clearest examples of putative proto Indo-European clusters of words in
the 1773-word set. To the extent that such groups of words tend to be defined by shared
consonant clusters, it is predictable that these longer words should appear more towards
the bottom of the rfm ranked list.
Our first conclusion with respect to proto Indo-European clusters in the English
lexicon is that they are a marginal special case in the more general systematicity
involving form and meaning. They are a special case because they are visible to the
ordinary linguistic intuitions of language users. They are marginal because the number
of clear groups of this kind is relatively small, and such groups as there are rarely
contain double-figure numbers of words. Nonetheless, their position towards the bottom
of the rfm ranking deserves comment. The words at the very bottom of the ranked list
have a negative correlation between their phonological and semantic distances to other
words. If a positive correlation can be seen as cementing the relationship between form
and meaning for a particular word, then a negative relationship is a tendency to
destabilise this relationship. Those words at that bottom of the ranked list would thus be
prime candidates for the relative dissociation of form and meaning; such words should
show a tendency towards changes in the phonological form of the word, and/or towards
semantic drift, with the principal meaning changing and acquiring other senses. These
tendencies may be offset for a particular word if it belongs to a group of words that
share some aspects of form and meaning. Thus, a largely archaic English word like
gloaming may only be interpretable for many English speakers because of its related
words gleam, glow, glare, … (implying “concerning light qualities”). Being in a small,
but appreciable group may confer some stability on a word.
Mental Lexicon
45
We might interpret these data as two complementary tendencies in the relation
between form and meaning in the mental lexicon: frequent words tend to rely on a very
large number of other, unrelated words to reinforce their relation between form and
meaning, whereas infrequent words tend to rely on a very small number of other closely
related words to sustain their meaning-form relationship.
Discussion and Conclusions
We have presented a novel approach to understanding some of the psychologically
relevant structure of the mental lexicon of English. This approach complements
behavioural experiments, etymological studies, and formal analysis directed to
understanding the mental lexicon, and it extends the corpus-based computational study
of language in a new direction by returning to the Saussurean idea that each word is
defined in opposition to all of the rest of the words in the lexicon. However, in our
approach we have replaced the notion of “opposition” with psychologically and
linguistically motivated representations of meaning and form, underwritten by the claim
that systematicity is an organizing principle fundamental to the human brain.
Our approach complements the connectionist cognitive modelling approach to
understanding the mental lexicon. The connectionist enterprise is based, in large part, on
the notion of superpositional storage. Despite the evidence of a degree of localisation in
lexical processing in the brain (see, e.g., Pulvermüller (1999) for a recent review), it is
nevertheless still true to say that any one word is typically associated with widespread
activity in many regions of the brain: the same processing substrate serves the
representation of many different words, and superpositional storage in connectionist
modelling has been seen as reflecting that fact. Our own approach has captured the
Mental Lexicon
46
aspect of superpositional storage in which the processing of each word is contingent on
the processing of all the rest of the words. However, the measures and the statistics we
have used have allowed us to see more clearly how each word may be embedded within
the lexicon, its form being implied by the rest of the meanings in the lexicon, and its
meaning being implied by the rest of the phonological forms in the lexicon.
We have not explored the range of possible phonological representations. The
ones we have used embody minimal assumptions about how the brain deals with
phonological representations of words. There have been claims in the literature for the
facilitatory influence of shared rimes (see, e.g., Radeau, Morais & Segui, 1995;
Slowiaczek, McQueen, Soltano & Lynch, 2000) and for the inhibitory effect of shared
onsets (Hamburger & Slowiaczek, 1996; Slowiaczek & Hamburger, 1992), both of
which suggest that the temporal nature of speech is critical to an understanding of the
processing of spoken words. In this study, in which we have been predominantly
concerned with monosyllabic, monomorphemic words, we have ignored the temporal
structure of words. Crucially, we have found significant systematicity in the mental
lexicon using our first and only measure of phonological distance. We predict that
measures of phonological distance that are more psychologically informed will yield a
clearer picture of this systematicity, with higher correlations. Psychologically more
realistic measures of phonological distance may be needed for the study of systematicity
in polysyllabic words.
Similarly, in this initial study we have not sought to explore the measure of
semantic distance. Context-based measures of semantic distance appear to be relatively
robust, but it is undoubtedly true that manipulation of parameters such as the number
and identity of the context words or the size of the context window would reveal a
Mental Lexicon
47
quantitative improvement in the behaviour of the measure of semantic distance, and a
subsequently clearer view of the systematicity in the mental lexicon.
Although Saussure’s observation concerning the essentially arbitrary
relationship between words and meanings is not threatened by our studies, there is
nonetheless more structure than we might initially have expected in the relationship. We
have shown that there is a tendency, at the level of simple words, towards the
compositionality that we take for granted at higher levels of linguistic structure. On
closer inspection, this compositionality is expressed as a systematicity that operates over
dimensions such as word length, complexity, and phonological content, even at the level
of monosyllabic, monomorphemic words.
We have identified categories of frequent, pragmatically important words as
beneficiaries of this systematicity. The sequence of words in spoken English
approximates to a rapid alternation between these (and similar) words and the
paradigmatic content words (the nouns, verbs and adjectives). We suggest that this
structure is adaptive. The core task in spoken communication is to create shared
semantic representations in the brains of the interlocutors. Punctuating the movement
between two content words with a word with a high ranking in the systematicity of the
lexicon effectively “resets” the mental lexicon, returning its overall state to a relatively
neutral position. Thus, it might be seen as adaptive to alternate between content words
and words towards the top of the rfm ranked list, compared with stringing together
several content words in succession. We might expect to find this same mechanism on
the smallest, simplest scale when two open class words are coordinated to produce a
compound, using a linking morpheme (see Krott, Baayen & Schreuder, 2001, for a
recent discussion). In English, we see this in the archaic device for joining together two
Mental Lexicon
48
content words with the functor morpheme /s/, as in woodsman, marksman and
spokesman, but the mechanism is productive in languages such as Dutch and German.
Before listing the conclusions we draw from the studies we report here, we
consider the cross-linguistic implications. Our prediction is that the systematicity within
the core of the mental lexicon should be a universal feature of human language. Initial
studies with Spanish, not reported here, suggest a quantitatively very similar
relationship between form and meaning.
Our conclusions are as follows.
(a) In methodological terms, we have demonstrated that it is possible to
investigate the structure of the mental lexicon by analysing the relationship between
form and meaning, applying conventional statistics to all of the word-level semantic and
phonological data points in the mental lexicon.
(b) Although the relationship between form and meaning is essentially arbitrary,
we have shown that for any one monosyllabic, monomorphemic word that relationship
is likely to be influenced by the rest of the lexicon.
(c) In terms of the functional architecture of the lexicon, we have demonstrated a
level of coherence with respect to semantic and phonological distances across the whole
of the monomorphemic, monosyllabic lexicon of English. This coherence suggests a
functional architecture in which the whole of the lexicon may be more or less involved
in the processing of any particular word, as implied by the notion of superpositional
storage.
(d) The mental lexicon has developed in line with the brain’s preference for
systematicity, exemplified in the many topographic maps used in solving evolutionarily
earlier problems. The lexicon of English has accommodated itself to the structural and
Mental Lexicon
49
processing requirements of the brain over many generations and we can detect the
effects in the relationship between form and meaning.
(e) The requirements of human communication also assert themselves in the
structure of the lexicon. Thus, subsets of communicatively important words seem to
reap the advantages of the systematicity in the mental lexicon.
(f) The proto-Indo-European clusters of words that have long been taken to be
examples of a structured relationship between form and meaning are less significant
than the general systematicity we have revealed.
(g) A measure showing how much each word individually participates in this
systematicity represents a principled and modality specific psycholinguistic processing
variable, that is a predictor of naming time.
(h) A general lowering of activation across the whole, systematically structured
lexicon provides a maximally parsimonious model of the impaired mental lexicon, and
one that predicts some of the qualitative effects of that impairment.
(i) The rapid alternation between high rfm and low rfm words in spoken English
may itself be adaptive for switching quickly between (typically low rfm) content words.
On the basis of these conclusions, we claim that the type of analysis of the lexicon we
have described represents a productive strategy for future research in further exploring
the general systematicity that we have revealed in the mental lexicon.
Mental Lexicon
50
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http://www.cstr.ed.ac.uk/projects/festival/
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Acknowledgements
The first author was supported by ESRC fellowship R/000/27/1244. The second
author was supported by a British Academy fellowship. The third author was supported
by Wellcome Trust grant GR064240AIA.
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Author Note
Richard Shillcock, Department of Psychology and Institute for Adaptive and
Neural Computation, Division of Informatics, University of Edinburgh.
Simon Kirby, Department of Theoretical and Applied Linguistics, University of
Edinburgh.
Scott McDonald, Institute for Adaptive and Neural Computation, Division of
Informatics, University of Edinburgh.
Chris Brew, Department of Linguistics, Ohio State University
Correspondence concerning this article should be addressed to Dr. Richard
Shillcock, Department of Psychology, 7 George Square, Edinburgh, EH8 9JZ, or to the
authors at rcs@cogsci.ed.ac.uk, simon@ling.ed.ac.uk, scottm@cogsci.ed.ac.uk,
cbrew@ling.ohio-state.edu, respectively.
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Footnotes
1. A summary of some of the data presented in this paper was presented to the
DiSS ’01 Disfluency in Spontaneous Speech ICSA workshop, August 2001,
Edinburgh.
2. http://www.cstr.ed.ac.uk/projects/festival
3. Throughout, our usage of the word “meaning” with reference to the analyses we
present might be replaced by a more conservative phrase such as “how the
words are used in context”.
4. http://info.ox.ac.uk/bnc/index.html
5. The choice of Pearson’s r, with its assumption of an interval scale, was
motivated by our strategy of using all the available data in our analyses. As we
make clear below, we wish to test the strong claim that the phonological and
semantic distances we have measured do indeed behave in an approximation to
interval scales. All statistical significances were calculated using randomisation
tests, thus avoiding those constraints on the use of r that are concerned with
significance testing.
6. Note that, since we are actually carrying out 1733 statistical tests at this point in
the analysis, we would expect some 17 of the correlations to be significant at p <
.01 by chance alone.
7. The position of her, the personal pronoun that occurs lowest in that category,
may reflect the fact that our analysis took no account of phonological reduction,
which might turn the citation form /h\\/ into /\\/.
8. All correlations reported are one-tailed unless specified otherwise.
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Table 1. Analysis of the correlation between semantic distances and phonological
edit-distances, partitioned by syntactic type (nouns and function words).
word set
N
r value
z-value
probability
(1-tailed)
all words
1733
.061
4.2
.001
nouns
1509
.066
4.3
.001
function words
99
.13
2.5
.006
In Tables 1–5 z-values are for 1000 randomisations, and p values are calculated directly
from the modelling. N.B. N is the number of words in each set, but correlations are
calculated across all the possible pairs of distances within that set.
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Table 2. Analysis of the correlation between semantic distances and phonological editdistances, for nouns partitioned by phonological length
word set
N
r value
z-value
probability
(1-tailed)
1 phone
10
-.33
-1.6
ns
2 phones
164
.002
0
ns
3 phones
780
.04
1.6
.05
4 phones
463
.032
1.9
.03
5 phones
88
.18
3.1
.001
6 phones
5
.025
.1
ns
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Table 3. Analysis of the correlation between semantic distances and phonological editdistances, for all words partitioned by word frequency (median split).
word set
N
r value
z-value
probability
(1-tailed)
Most frequent
866
.037
1.9
.03
Least frequent
867
.069
3.4
.001
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Table 4. Analysis of the correlation between semantic distances and phonological editdistances for all words, divided into sets ranked by word frequency.
Word set
N
r value
z-value
probability
(1-tailed)
1-200
200
.13
3.3
.001
201-400
200
.0067
.2
ns
401-600
200
-.003
-.1
ns
601-800
200
.037
.9
ns
801-1000
200
-.0022
0
ns
1001-1200
200
.11
2.4
.008
1201-1400
200
.027
.7
ns
1401-1600
200
.14
3.2
.001
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Table 5. Analysis of the correlation between semantic distances and phonological editdistances for the nouns ranked by word frequency.
word set
N
r value
z-value
probability
(1-tailed)
1 – 200
200
.069
1.7
.04
201 – 400
200
-.017
-.37
ns
401 – 600
200
+.028
.60
ns
601 – 800
200
-.009
.18
ns
801 – 1000
200
.097
2.2
.01
1001 – 1200
200
.045
1.1
ns
1201 – 1400
200
.15
3.3
.001
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Figure 1. Dendrogram constructed from the application of hierarchical cluster analysis
to the pairwise phonological distance matrix for 40 randomly selected four-phone
words.
Figure 2. Dendrogram constructed from the application of hierarchical cluster analysis
to the pairwise semantic distance matrix for the 40 randomly selected four-phone words
shown in Figure 1.
Figure 3. The distribution of phonological edit distances of different length between
each word and every other of the 1733 words.
Figure 4. The relation between mean semantic distance and phonological edit-distance,
connected by a single line for clarity.
Figure 5. Individual words’ value of rfm plotted against rank position. The curve is
displaced from 0, reflecting the overall positive correlation between form and meaning.
Figure 6. The distribution, plotted by decile in the ranking by rfm, of (a) editing terms,
(b) words that often serve as proper names for people, (c) swear words, and (d) personal
pronouns.
Figure 7. The distribution of the str-initial words, plotted by decile. Items that do not
currently seem to belong to the string-type category are marked with a question mark.
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Figure 8. The distribution of the ump-final words, plotted by decile. Items that do not
currently seem to belong to the ump-type category are marked with a question mark.
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Figure 1.
74
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Figure 2.
75
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76
Figure 3.
Number of occurrences
200000
150000
100000
50000
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Phonological distance
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77
Figure 4
0.7
Semantic distance
0.65
0.6
0.55
0.5
0.45
0.4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Phonological distance
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Figure 5.
78
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Figure 6.
79
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80
Figure 7.
14
strike (?)
strong (?)
straw
strict (?)
stress
strive (?)
strange (?)
number of str- words
12
10
8
street
stroll (?)
string
stripe
6
4
2
stretch
strip
stream
strap
stroke
straight
strain
strand
stride
streak
stray (?)
0
1
2
3
4
5
6
decile
7
8
9
10
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81
Figure 8.
number of -ump words
5
4
pump (?)
plump
bump
slump
3
dump
lump
jump (?)
2
1
0
1
2
3
4
5
6
decile
7
8
9
10
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Appendix A
The top 50 items in the 1733 words ranked by rfm.
1
oh
0.18926
2
you
0.18192
3
er
0.17949
4
and
0.17792
5
ah
0.17712
6
ye
0.17648
7
mick
0.17502
8
ma
0.17387
9
yeah
0.17118
10
off
0.17081
11
we
0.16932
12
it
0.16895
13
eh
0.16789
14
e
0.16511
15
me
0.16494
16
fuck
0.16487
17
do
0.16485
18
shit
0.16312
19
aye
0.16206
20
if
0.16114
21
hook
0.1602
82
Mental Lexicon
22
is
0.16003
23
mum
0.15841
24
I
0.15769
25
bitch
0.15769
26
us
0.15703
27
dear
0.15594
28
jay
0.15583
29
word
0.15546
30
cook
0.15519
31
bit
0.15489
32
chuck
0.15427
33
nick
0.15416
34
kick
0.15362
35
up
0.15322
36
know
0.15254
37
wee
0.15157
38
miss
0.15135
39
would
0.15121
40
he
0.15034
41
bye
0.15021
42
zoo
0.1483
43
she
0.14748
44
ha
0.14744
45
bet
0.14739
83
Mental Lexicon
46
stretch
0.14701
47
what
0.14691
48
u
0.14582
49
flick
0.14515
50
tick
0.14427
84
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Appendix B
The last 50 items in the 1733 words ranked by rfm.
1683
plague
-0.061419
1684
waist
-0.061565
1685
clash
-0.062143
1686
pledge
-0.062543
1687
hold
-0.062664
1688
prime
-0.063273
1689
grand
-0.063387
1690
bloke
-0.063425
1691
stress
-0.063509
1692
stage
-0.065427
1693
round
-0.065508
1694
script
-0.065714
1695
quite
-0.065901
1696
tribe
-0.066213
1697
plan
-0.066658
1698
spoil
-0.066804
1699
ground
-0.066818
1700
drought
-0.066843
1701
please
-0.067156
1702
crime
-0.069045
1703
bolt
-0.069167
1704
blunt
-0.069376
85
Mental Lexicon
1705
trade
-0.069684
1706
blast
-0.070118
1707
blame
-0.070786
1708
prompt
-0.071472
1709
fold
-0.071703
1710
proud
-0.072214
1711
quote
-0.072302
1712
claim
-0.072448
1713
cold
-0.073592
1714
priest
-0.07459
1715
glance
-0.075296
1716
twice
-0.076163
1717
drunk
-0.076982
1718
price
-0.07772
1719
prince
-0.078744
1720
blind
-0.079789
1721
plight
-0.081207
1722
clerk
-0.081327
1723
plus
-0.081978
1724
state
-0.084518
1725
wind
-0.088635
1726
trust
-0.090494
1727
strive
-0.090784
1728
franc
-0.09411
86
Mental Lexicon
1729
strange
-0.104583
1730
bounce
-0.106148
1731
friend
-0.110286
1732
twelve
-0.113266
1733
frank
-0.114982
87
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Appendix C.
The swear words of the corpus and their values of rfm.
fuck
.165
shit
.163
bitch
.158
dick
.137
piss
.132
cock
.132
hell
.111
god
.108
come
.100
damn
.095
screw
.066
arse
.053
ball(s)
.033
lay
-.025
shaft
-.035
crap
-.045
88