Title: "Can Human Verb Associations help identify Salient Features for Semantic Verb Classes?"


Abstract:

Different frameworks of semantic verb classes depend on different
instantiations of semantic similarity, e.g. Levin relies on verb
similarity referring to syntax-semantic alternation behaviour, WordNet
uses synonymy, and FrameNet relies on situation-based agreement as
defined in Fillmore's frame semantics (Fillmore, 1982). As an
alternative to a resource-intensive manual classifications, automatic
methods such as classification and clustering are applied to induce
verb classes from corpus data, e.g. Schulte im Walde (2000), Merlo and
Stevenson (2001), Joanis and Stevenson (2003), Korhonen, Krymolowski
and Marx (2003), Stevenson and Joanis (2003), Schulte im Walde (2003).

The verb feature selection on which an automatic classification relies
should model the similarity of interest. However, in larger-scale
classifications which model verb classes with similarity at the
syntax-semantics interface, it is not clear which features are the
most salient. The verb features need to relate to a behavioural
component (modelling the syntax-semantics interplay), but the set of
features which potentially influence the behaviour is large, ranging
from structural syntactic descriptions and argument role fillers to
adverbial adjuncts. In addition, it is not clear how fine-grained the
features should be; for example, how much information is covered by
low-level window co-occurrence vs. higher-order syntactic frame
fillers?

In this talk, I investigate whether human associations to verbs as
collected in a web experiment can help us to identify salient verb
features for semantic verb classes. Assuming that the associations
model aspects of verb meaning, we apply an unsupervised clustering to
the verbs, as based on the associations, and validate the resulting
verb classes against standard approaches to semantic verb classes,
i.e. GermaNet and FrameNet. Then, various clusterings of the same
verbs are performed on the basis of standard corpus-based types, and
evaluated against the association-based clustering as well as GermaNet
and FrameNet classes. We hypothesise that the corpus-based clusterings
are better if the instantiations of the feature types show more
overlap with the verb associations, and that the associations
therefore help to identify salient feature types.