PANEL ON WORDNET RELATIONS
WordNet Relations revisited: Panel Discussion on recent
approaches to account for various kinds of relations in WordNets
and WordNet-like resources
PANEL PROGRAMME

Introduction by Claudia Kunze (Panel Organizer)

Extending GermaNet with syntagmatic relations
(Lothar Lemnitzer, University of Tübingen, Germany)
Abstract 1

Inducing taxonomies of attribute concepts of adjectves from corpora Aiming at supporting manual development of ontologies
(Kyoko Kanzaki, NICT, Japan)
Abstract 2

Extending WordNets to all the main POS: specification of cross-POS
relations to encode adjectives
(Sara Mendes, University of Lisboa, Portugal)
Abstract 3

Relations: Are we missing something?
(Christiane Fellbaum, Princeton University, USA)
Abstract 4

Acquiring semantic relations by harvesting and interpreting noun-noun
compounds
(Tony Veale, UCD, Ireland)
Abstract 5

WordNet and formal ontology
(Adam Pease, Articulate Software, USA)
Abstract 6

Annotating WordNet Synsets by Sentiment-Related Information: Issues
and Potential Solutions
(Andrea Esuli, ISTI-CNR, Italy)
Abstract 7

Subjectivity mark-up in WordNet: does it work cross-lingually? A case
study on Romanian WordNet
(Dan Tufis, RACAI, Romania)
Abstract 8
ABSTRACTS
Abstract 1:
Extending GermaNet with syntagmatic relations (Lothar Lemnitzer)
I will report about work done for the extension of the German WordNet GermaNet.
We are currently extending the WordNet with relations between the verbal head and the
head of the direct object. The data have been extracted from a large, partially parsed
German corpus. The word pairs have been ranked by the maximum likelihood statistics
which indicates the significance of the co-occurrence of both words. Our approach is
comparable to that of Bentevogli und Pianta ("Extending WordNet with Syntagmatic
Information"), but has a clearer focus on the relations.
In my presentation I will shortly explain the application context in which the work is
done, depict the current state of the work, and outline our plans for evaluating the
extended WordNet.
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Abstract 2
Inducing taxonomies of attribute concepts of adjectves from corpora - Aiming at
supporting manual development of ontologies (Kyoko Kanzaki)
With the aim of compiling an objective thesaurus of adjectives, we extracted nouns that
refer to attribute concepts of adjectives from corpora, manually evaluated the capability
to extract the "attribute-instance" relations, and then automatically distributed the
obtained attribute concepts on the semantic map. In our experiments we obtained the
taxonomic structure (consisting of similarity and hierarchical relations) like a 3D-map in
which nouns are distributed according to their similarity. This work is still underway, but
the map shows the possibilities of improving the lexicons made by humans.
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Abstract 3
Extending WordNets to all the main POS: specification of cross-POS relations to
encode adjectives (Sara Mendes)
Extending WordNets to all the main POS involves revision of certain commonly used
relations and the specification of new ones. Encoding adjectives in WordNets, for
instance, calls for the specification of a number of cross-POS relations.
Since the semantic organisation of adjectives seems to be unlike that of nouns and
verbs, as this POS does not show a hierarchical organisation (cf. Fellbaum et all (1993)
and Miller (1998)), in WordNet.PT we use a small set of semantic relations mirroring
adjectives definitional features in the database. It is undeniable that important structural
information can be extracted from the hierarchical organisation of lexical items, namely
of nouns and verbs. However, extending WordNets to all the main POS involves revision
of certain commonly used relations and the specification of several cross-POS relations.
Some of the relations used in WordNet.PT are semantic relations introduced in Princeton
WordNet, but there are also some new pointers, which allow a strongly empirically
motivated encoding of adjectives in the database. These relations, not only allow us to
make adjective classes emerge, but they also conform to some complex phenomena (cf.
Marrafa (2005) and Marrafa & Mendes (2006) for a detailed discussion on representing
and encoding LCS deficitary verbs in WordNet-like lexica).
As shown by word association tests, antonymy is a basic relation in the organisation of
descriptive adjectives. Nonetheless, this relation does not correspond to conceptual
opposition, which is one of the semantic relations generally used for the definition of
adjective clusters. We argue that conceptual opposition does not have to be explicitaly
encoded in WordNets, as it is possible to infer it from the combination of synonymy and
antonymy relations. Still with regard to descriptive adjectives, and to put it somewhat
simplistically, these adjectives ascribe a value of an attribute to a noun. Attributes are
generally lexicalised by nouns. Hence we use a cross-POS relation to link each descriptive
adjective to the noun lexicalising the attribute it modifies. This generally corresponds to
the is a value of/attributes relation, used in Princeton WordNet. We use a different label
for this semantic relation to make it more straightforward to the common user:
charaterises with regard to/can be characterised by. As to relational adjectives, these
entail more complex and diversified relations between the set of properties they
introduce and the modified noun, often pointing to the denotation of another noun. In
order to encode this relation between the relational adjective and the noun which
lexicalises the set of properties the adjective points to, we use the is related to semantic
relation.
This small set of relations allows us to encode the basic features of property ascribing
adjectives in WordNets, while making it possible to derive membership of encoded
adjectives to the descriptive and relational adjective classes, from the relations
expressed in the network. Another issue regarding adjectives is that they have a rather
sparse net of relations. We introduce a new relation to encode salient characteristics of
nouns, often expressed by adjectival expressions: is characteristic of/has as a
characteristic to be. Although we can discuss the status of this relation in terms of lexical
knowledge, it is undeniable it regards crucial information for many WordNet-based
applications, namely those using inference systems. Also, as the network becomes
denser, it contributes to richer and clearer synsets.
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Abstract 4
Relations: Are we missing something? (Christiane Fellbaum, joint work with
Jordan Boyd-Graber, Daniel Osherson, Rob Schapire)
Although many semantic and lexical relations have been proposed for WordNets in many
languages, one might still wonder whether important connections that are intuitively
obvious are overlooked. For example, WordNet has no way to link between members of
such pairs as "Thanksgiving" and "turkey," "dollar" and "green," "chopstick" and "Chinese
restaurant." Purely statistical corpus analyses could find some, but not all such intuitively
related pairs and would moreover identify many spurious ones.
We performed an experiment aimed at increasing WordNet's connectivity by identifying
syntagmatic links among synsets in ways that that did not introduce biases and
limitations inherent to traditional, systematic, introspectively defined relations. We
collected human ratings that reflect the assiociative strength among frequent and salient
concepts. We obtained directed and weighted ratings of similarity for concept pairs.
Comparing the results with standard measures of semantic similarity, we found that our
evocation method captures similarities that elude these measures. The results raise
questions as to the nature of semantic relations, semantic similarity, and human
conceptual organization.
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Abstract 5
Acquiring semantic relations
compounds (Tony Veale)
by
harvesting
and
interpreting
noun-noun
A noun-noun compound is a noun-phrase in which the underlying semantic relation has
been elided, as in "pepper mill", "pizza oven" and "claw hammer". Though WordNet
contains a substantial number of noun compounds, this set is just a tiny fragment of the
space of compounds in common English usage. Moreover, WordNet does not provide a
semantic interpretation for the compounds it contains (though WordNet's meronymy
network can be used to understand some part-whole compounds, such as "car engine").
By using corpus-based techniques to interpret common noun-noun compounds, we can
augment WordNet in a variety of ways: first, by acquiring new compound lexical entries;
second, by acquiring semantic interpretations for these compounds, from which simple
textual glosses can be automatically generated; and thirdly, by acquiring specific
semantic relations (such as "X grinds Y" for "pepper mill") to connect specific word
senses. Since even highly specific nouns like "knife" exhibit different properties in
different contexts (e.g., a knife can be used to cut, carve, spread, serve and even paint),
we argue that noun compounds provide the best context in which to understand the
relational potential of nouns.
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Abstract 6
WordNet and formal ontology (Adam Pease)
This short talk will discuss the distinctions between formal ontology and WordNet.
Although the hierarchical organization implicit in word senses looks very much like an
ontology, it is in fact very different. Also discussed will be the different criteria for
inclusion of nodes and arcs (synsets and relations) in both a lexical product and a formal
ontology. The talk will present specific results from the ongoing effort to map the
Suggested Upper Merged Ontology and WordNet.
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Abstract 7
Sentiment analysis for WordNet (Andrea Esuli)
Many works in sentiment analysis have focused on the problem of subjectivity detection,
at various levels: from terms (or term senses), as in the automatic annotation of lexical
resources, to fragments of text, as in opinion extraction, to entire documents, as in
sentiment classification. At all these levels, the two dimensions that have been
investigated
more
actively
are
polarity
("positive/negative")
and
force
("strong/mild/weak" expression of positivity or negativity). In the SentiWordNet project
we made a first attempt at automatically adding information concerning these two
dimensions to WordNet. In another, more recent research we have explored a further
dimension of subjective language, i.e, attitude type, which distinguishes, for example,
between moral appreciation ("honest") and aesthetic appreciation ("beautiful").
We think that endowing WordNet with annotations pertaining to these three dimensions
(polarity + force + attitude type) would make WordNet an even more invaluable resource
for sentiment analysis.
Adding this information to WordNet would not be an easy task, for at least two reasons.
One is the sheer size of the resource; this might call, at least initially, for a semiautomatic approach, on the line of the SentiWordNet or of the "WordNet Evocation"
projects. The other is the choice of the taxonomy of sentiment types, which needs to
compromise between conceptual subtlety and real-world applicability. For our recent
work on attitude type we have adopted a taxonomy of attitude types originally defined in
Martin and White's Appraisal Theory; however, other potentially interesting alternatives
have been developed, e.g. in the EU-funded Simple project.
However, we conjecture that even this three-dimensional specification of the sentimentrelated properties of synsets might not be sufficient for application purposes, at least for
some parts of speech. For example, it is conceivable that a verb's polarity should not be
characterized as positive or negative tout court, but that a distinction should be made For
instance, the verbs "torture" and "discard" both have a negative slant; however, while
"torture" casts a negative character on the subject of the action (and on the action
itself), "discard" typically casts a negative character on the direct object of the action.
Such distinctions should be accounted for in a lexicon, especially in order to make it
useful for opinion extraction applications.
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Abstract 8
Subjectivity mark-up in WordNet: does it work cross-lingually? A case study on
Romanian WordNet (Dan Tufis)
The textual information on Web can be roughly classified as facts and opinions (or
subjective assessments). The subjectivity analysis is currently a research topic with many
applications in so-called social net for finding users' personal experiences and opinions on
various subjects, from commercial products to political events. Word-of-mouth on the
Web is taken seriously by policy/decision makers.
There are various ways to model the processes of opinion mining and opinion
classifications and different granularities at which these models are defined (documents
vs. sentences). For instance, in reviews classification one would try to assess the overall
sentiment of an opinion holder with respect to a product (positive, negative and possibly
neutral). However, the document level sentiment classification is too coarse for most
applications and therefore the most advanced opinion miners are considering the
sentence level. Thus, a first task becomes detecting the opinionated sentences by classify
them as either objective or subjective.
Irrespective of the methods and algorithms (which are still in their infancy) used in
subjectivity analysis, they exploit the pre-classified words and phrases as opinion or
sentiment bearing lexical units. Such lexical units (also called senti-words, polar-words)
are manually specified, extracted from corpora or marked-up in the lexicons (as in SentiWordNet).
While opinionated status of a sentence is less controversial, its polarity might be rather
problematic. The issue is generated by the fact that the polarity of many senti-words
depend on context (some time on local context some time on global context. Apparently,
bringing into discussion the notion of sense (as Senti-WordNet does) solves the problem
but this is not so. For instance the polarity of many modifiers (adjectives and adverbs)
depends on the modified lexical unit. Consider the adjective "long" sense no.1 (primarily
temporal sense; being or indicating a relatively great or greater than average duration or
passage of time or a duration as specified) which is (in SUMO terms) a Subjective
Assessment Attribute and has the following subjectivity/polarity mark-up: P:0.0;
N:0.125; O:0.875. This would imply that long:1 carries a negative connotation. While
this is true for a sequence "the response time is long" this is not the case in "the engine
life is long". It would really help to have, in case of modifiers, a special type of relation
"Typically-modifies" and to have the subjectivity/polarity mark-up attached to this
relation. This idea would make a distinction between words which intrinsically bearing a
specific subjectivity/polarity and the words the polarity of which should be relationally
considered.
A distinct issue is related to cross-lingual validity of the Senti-WordNet annotation. We
claim that given that lexical semantics is not culturally-unbound, mechanical transfer of
the subjectivity mark-up from the English synsets to other's language translation
equivalence synsets might be problematic. For instance, (cf Kim & Myaeng, NTCIR 2007)
“a sentence in Japanese, reporting on a rapid merge of two companies should be judged
to have negative sentiment whereas the same kind of activities in the US would be a
positive event”. In spite of this, we argue that Senti-WordNet is a very useful resource,
which requires carefully designed methodologies and algorithms (well beyond the bag-of
words current practices) able to fully exploit the sentiment annotations. Our experiments
in Romanian, although in an early phase, show that the majority of subjectivity
annotations (especially those for nouns and verbs) do hold cross-lingually.