Can Human Verb Associations Help Identify Salient Features for Semantic Verb Classification?

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 69–76, New York City, June 2006. c⃝2006 Association for Computational Linguistics Can Human Verb Associations Help Identify Salient Features for Semantic Verb Classification? Sabine Schulte im Walde Computational Linguistics Saarland University Saarbr¨ucken, Germany schulte@coli.uni-sb.de Abstract This paper investigates whether human as- sociations 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 as- pects of verb meaning, we apply a clus- tering to the verbs, as based on the as- sociations, and validate the resulting verb classes against standard approaches to se- mantic 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 eval- uated against the association-based clus- tering as well as GermaNet and FrameNet classes. We hypothesise that the corpus- based clusterings are better if the instan- tiations of the feature types show more overlap with the verb associations, and that the associations therefore help to identify salient feature types. 1 Introduction There are a variety of manual semantic verb clas- sifications; major frameworks are the Levin classes (Levin, 1993), WordNet (Fellbaum, 1998), and FrameNet (Fontenelle, 2003). The different frame- works depend on different instantiations of seman- tic 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 alterna- tive to the resource-intensive manual classifications, automatic methods such as classification and clus- tering are applied to induce verb classes from cor- pus data, e.g. (Merlo and Stevenson, 2001; Joanis and Stevenson, 2003; Korhonen et al., 2003; Steven- son and Joanis, 2003; Schulte im Walde, 2003; Fer- rer, 2004). Depending on the types of verb classes to be induced, the automatic approaches vary their choice of verbs and classification/clustering algo- rithm. However, another central parameter for the automatic induction of semantic verb classes is the selection of verb features. Since the target classification determines the sim- ilarity and dissimilarity of the verbs, the verb fea- ture selection should model the similarity of inter- est. For example, Merlo and Stevenson (2001) clas- sify 60 English verbs which alternate between an in- transitive and a transitive usage, and assign them to three verb classes, according to the semantic role as- signment in the frames; their verb features are cho- sen such that they model the syntactic frame alterna- tion proportions and also heuristics for semantic role assignment. In larger-scale classifications such as (Korhonen et al., 2003; Stevenson and Joanis, 2003; Schulte im Walde, 2003), 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 com- ponent (modelling the syntax-semantics interplay), but the set of features which potentially influence the behaviour is large, ranging from structural syn- tactic descriptions and argument role …

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