Salience and Frequency of
Meanings
Comparison of Corpus and
Experimental Data on Polysemy
Daniela Marzo, Verena Rube, Birgit Umbreit
(University of Tübingen)
Corpus and Cognition Workshop, Corpus Linguistics Conference 2007
Structure
1.
2.
3.
4.
5.
6.
Hypotheses
Evidence from other studies
Production experiment
Corpus analysis
Comparison
Résumé
2
1. Hypotheses
Hypothesis 1:
Frequency = Entrenchment (≈ Salience)
Schmid (2000):
From-Corpus-to-Cognition Principle,
compare: Langacker (1991)
3
Hypothesis 2:
Frequency ≠ Salience
(Gernsbacher 1984, De Mauro 1980)
4
2. Evidence from previous studies

Roland & Jurafsky (2002), Gilquin (2006 and
2007)
- comparison of corpus studies and
experiments on polysemy
→ differences in verb sense distributions
→ Evidence against Hypothesis 1
5
- But Gilquin (2007) claims that there are
some links between salience and frequency:
If phrasal and colloquial meanings are
excluded from her analysis of to take,
the ‘move’ sense is most prominent in the
corpus and in the experiment.
6
-
Gilquin (2007) also claims that the
prototypical (= most salient) sense has
to be a concrete sense.
7
3. Production experiment

Sentence Generation Task (SGT)
Subjects are asked to produce sentences
disambiguating the meanings of the stimulus
(Caramazza and Grober, 1976; Colombo and Flores
d’Arcais, 1984; Raukko, 2003)

Definition Task (DT)
Sentences should be accompanied by
definitions (Raukko 2003)
8
Questionnaire
9
Example: Disambiguation by sentences

(1) Quel bimbo diventerà grande in fretta
(crescere).
This child will get big very soon (to grow).

(2) Ha un grande appartamento (di vaste
dimensioni).
He has got a big apartment (of big dimensions).
10
Example: Disambiguation by sentences
Two closely related meanings
- ‘spatially extended’
- ‘tall, extended in height’
were disambiguated by the informants.
11
Example: Disambiguation by definition

(3) Quel cantante è grande.
This singer is ?.
Occurrence is ambiguous:
1. ‘tall, extended in height’ ?
or
2. ‘admirable, famous’ ?
or
3. ‘very able, very gifted’?
12
Example: Disambiguation by definition

The definition disambiguates the sentence:
Quel cantante è grande (famoso).
 2. ‘admirable, famous’
13
Comparison sample



400 stimuli
20 stimuli per questionnaire
30 informants per questionnaire
Comparison sample:
Results for the 15 most frequent words:
andare, avere, cosa, dire, dare, dovere, essere, fare,
grande, potere, sapere, stare, vedere, venire,
volere
14
4. Corpus Analysis (CA)

Corpus of the Lessico di frequenza
dell‘italiano parlato (LIP)
-
-
-
Corpus of transcribed spoken language
easily accessible via the banca dati dell’italiano
parlato (BADIP)
ca. 490000 words
5 different types of oral texts: telephone
conversations, university lectures, etc.
recordings were made in 4 Italian cities: Florence,
Milan, Rome and Naples.
15
Constituting the Subcorpus


For each of the 15 sample words: 50
randomly selected occurrences of the
Florence subcorpus (10 per text type)
~ average number of sentences in the
SG&DT
Only occurrences of the original stimulus! (No
idioms, etc.)
16
5. Comparison

(i) The most salient meanings in the SG&DT
in the majority of the cases (9/15) correspond
to the most frequent meanings of the CA.
venire, andare, dire, dovere, essere, fare,
potere, sapere, vedere
17
Comparison for sapere
70
60
50
40
30
20
10
0
Experiment
Corpus
.
t
.
f
ll
g
sth d ou o sth stand ean te o sme uage st to uous anin
w
e
s
m
r to
in
d
o
ta to lang att big me
kn to f e to unde
o
t
to am ew
o
f#
t
bl to
o
n
a
e
nd
no
b
a
m
to
om
c
d
oo
g
a
ve
a
h
to
18
Comparison for essere
50
40
30
20
10
0
Experiment
Corpus
s NM
c ) ti tyj)ec te) red) e ofe ase anng toc ost x isiton)al lepdpensureher) ti ong)ouN
i
t
s
e
n
i
m
a t a i
s
ter(i dect obewehmea sa mto be lo to toofesbetoc hame(weaindi camb
c
ra e a m b h
tobe e
to
(pr to
ha to abb setr (s o to be t
e
c
o + tim
b
t
+
b
+
o
(
t
to
e ted ( to
e(
b
b
to oc a
to
l
be
to
19

(ii) If the most important meaning in one data
type does not correspond to the most
important meaning in the other data type, it is
likely to correspond to the second most
important meaning. Sometimes this works in
both directions.
cosa, avere, dare, grande, stare, volere
20
Comparison for grande
40
35
30
25
20
15
Experiment
Corpus
10
5
0
t
s
s
e
p
d
n
un
ing
ted
ou
ag
ee guou
tio
si ty nde
o
n
n
d
a
#
/
an
m
e
e
m
u
i
e
l
a
h
e
t
t
t
f
g
i
b
a
a
i
g
n
m
S
b
~
#
ex
,t
hi
,i
am
le
ig
ed
l ly
ew
e#
t
act
b
b
l
a
n
f
r
i
i
a
t
b
t
g
a
no
bs
mir suita
sp
be
#a
ad
o
t
g
bi
21

(iii) The less frequent meanings in the CA
and the less salient meanings in the SG &
DT usually diverge.
22
Comparison for essere
50
40
30
20
10
0
Experiment
Corpus
s NM
c ) ti tyj)ec te) red) e ofe ase anng toc ost x isiton)al lepdpensureher) ti ong)ouN
i
t
s
e
n
i
m
a t a i
s
ter(i dect obewehmea sa mto be lo to toofesbetoc hame(weaindi camb
c
ra e a m b h
tobe e
to
(pr to
ha to abb setr (s o to be t
e
c
o + tim
b
t
+
b
+
o
(
t
to
e ted ( to
e(
b
b
to oc a
to
l
be
to
23
Importance of concrete meanings
In the cases of result (ii):
Experiment: most important meaning =
“concrete” meaning
Corpus: most important meaning =
“abstract” meaning
e.g. cosa
Corpus:
‘object #abstract’ 84 %
Experiment: ‘object #concrete’ 39 %
24
⇒ confirmation of the claim that the
“prototypical” sense is a concrete sense
⇒ the importance of concrete meanings in the
cognitive system seems to inhibit the total
matching of the results
25
5. Résumé
The comparison study confirmed


No 1:1 correspondence between experiment and
corpus data
⇒ frequency ≠ salience (Hypothesis 2)

BUT: In almost two thirds of the data the most
important meanings in CA and in SG&DT are
identical. If they do not match there is
correspondence to the second most important
meaning.
⇒ frequency ≈ salience
(Tendency towards Hypothesis 1 ?)
26
Comparability of the evidence?
Production
Corpus
task data = language reflection data
data = language use data
27
THANK YOU FOR YOUR ATTENTION!
28
References
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
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
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Caramazza, A. and E. Grober (1976) Polysemy and the structure of the
subjective lexicon, in C. Rameh (ed.) Semantics: Theory and
Application. Georgetown University Round Table on Linguistics 1976,
pp.181-206. Washington D.C.: Georgetown University Press.
Colombo, L. and G. B. Flores d’Arcais (1984) ‘The meaning of Dutch
prepositions: Psycholinguistic study of polysemy’. Linguistics 22, 51-98.
De Mauro, T. (1980) Guida all’uso delle parole. Come parlare e scrivere
semplice e preciso. Uno stile italiano per capire e farsi capire. Roma:
Editori Riuniti.
De Mauro, T. and F. Mancini (1993) Lessico di frequenza dell’italiano
parlato. Milano: Etas Libri.
Gernsbacher, M.A. (1984) ‘Resolving 20 years of inconsistent
interactions between lexical familiarity and orthography, concreteness,
and polysemy’. Journal of Experimental Psychology 113, 256-81.
Gilquin, G. (2006) Towards an empirically grounded definition of
prototypes, Poster Presentation at Linguistic Evidence II, Tübingen, 2 4 February 2006.
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References






Gilquin, G. (2007) Universality and language specificity in
prototypicality. Paper presented at the AFLiCo conference, Lille, 10 -12
May 2007.
Juilland, A. and V. Traversa (1973) Frequency dictionary of Italian
words. The Hague: Mouton de Gruyter.
Langacker, R. W. (1991) Foundations of Cognitive Grammar. Vol. II.
Stanford: Stanford University Press.
Raukko, J. (2003) Polysemy as flexible meaning: experiments with
English get and Finnish pitää, in B. Nerlich et al. (eds.) Polysemy:
Flexible Patterns of Meaning in Mind and Language, pp. 161-93. Berlin:
Mouton de Gruyter.
Roland, R. and D. Jurafsky (2002) Verb sense and verb
subcategorization probabilities, in Stevenson, S. and P. Merlo (eds.)
The Lexical Basis of Sentence Processing: Formal, Computational, and
Experimental Issues, pp. 325-46. Amsterdam: Benjamins.
Schmid, H. J. (2000) English Abstract Nouns as Conceptual Shells.
From Corpus to Cognition. Berlin: Mouton de Gruyter.
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