Learning Verb Argument Structure from Minimally Annotated Corpora

Learning Verb Argument Structure from Minimally Annotated Corpora∗ Anoop Sarkar and Woottiporn Tripasai Dept. of Computer and Information Science University of Pennsylvania 200 South 33rd Street, Philadelphia, PA 19104-6389 USA {anoop,tripasai}@linc.cis.upenn.edu Abstract In this paper we investigate the task of automatically identifying the correct argument structure for a set of verbs. The argument structure of a verb allows us to predict the relationship between the syntac- tic arguments of a verb and their role in the under- lying lexical semantics of the verb. Following the method described in (Merlo and Stevenson, 2001), we exploit the distributions of some selected fea- tures from the local context of a verb. These fea- tures were extracted from a 23M word WSJ cor- pus based on part-of-speech tags and phrasal chunks alone. We constructed several decision tree classi- fiers trained on this data. The best performing clas- sifier achieved an error rate of 33.4%. This work shows that a subcategorization frame (SF) learning algorithm previously applied to Czech (Sarkar and Zeman, 2000) is used to extract SFs in English. The extracted SFs are evaluated by classifying verbs into verb alternation classes. 1 Introduction The classification of verbs based on their underlying thematic structure involves distinguishing verbs that take the same number and category of arguments but assign different thematic roles to these argu- ments. This is often termed as the classification of verb diathesis roles or the lexical semantics of pred- icates in natural language (see (Levin, 1993; Mc- Carthy and Korhonen, 1998; Stevenson and Merlo, 1999; Stevenson et al., 1999; Lapata, 1999; Lapata and Brew, 1999; Schulte im Walde, 2000)). Fol- lowing the method described in (Merlo and Steven- son, 2001; Stevenson and Merlo, 1999; Stevenson et ∗ This research was supported in part by NSF grant SBR-89- 20230. Thanks to Paola Merlo, Dan Gildea, David Chiang, Ar- avind Joshi and the anonymous reviewers for their comments. Also thanks to Virginie Nanta for an earlier collaboration with the fi rst author on an unsupervised version of this work. al., 1999), we exploit the distributions of some se- lected features from the local context of a verb but we differ from these previous studies in the use of minimally annotated data to construct our classifier. The data we use is only passed through a part-of- speech tagger and a chunker which is used to iden- tify base phrasal categories such as noun-phrase and verb-phrase chunks to identify potential arguments of each verb. Lexical knowledge acquisition plays an impor- tant role in corpus-based NLP. Knowledge of verb selectional preferences and verb subcategorization frames (SFs) can be extracted from corpora for use in various NLP tasks. However, knowledge of SFs is often not fine-grained enough to distinguish vari- ous verbs and the kinds of arguments that they can select. We consider a difficult task in lexical knowl- edge acquisition: that of finding the underlying ar- gument structure which can be used to relate the ob- served list of SFs of a particular verb. The task in- …

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