On Learning more Appropriate Selectional Restrictions

On Learning more Appropriate Selectional Restrictions Francesc Ribas* Departament de Llenguatges i Sistemes Informktics Universitat Polit~cnica de Catalunya Pau Gargallo, 5 08028 Barcelona Spain ribas©isi, upc. es Abstract We present some variations affecting the association measure and thresholding on a technique for learning Selectional Re- strictions from on-line corpora. It uses a wide-coverage noun taxonomy and a statistical measure to generalize the ap- propriate semantic classes. Evaluation measures for the Selectional Restrictions learning task are discussed. Finally, an experimental evaluation of these varia- tions is reported. Subject Areas: corpus-based language modeling, computational lexicography 1 Introduction In recent years there has been a common agree- ment in the NLP research community on the im- portance of having an extensive coverage of selec- tional restrictions (SRs) tuned to the domain to work with. SRs can be seen as semantic type con- straints that a word sense imposes on the words with which it combines in the process of seman- tic interpretation. SRs may have different ap- plications in NLP, specifically, they may help a parser with Word Sense Selection (WSS, as in (Hirst, 1987)), with preferring certain structures out of several grammatical ones (Whittemore et al., 1990) and finally with deciding the semantic role played by a syntactic complement (Basili et al., 1992). Lexicography is also interested in the acquisition of SRs (both defining in context ap- proach and lexical semantics work (Levin, 1992)). The aim of our work is to explore the feasibil- ity of using an statistical method for extracting SRs from on-line corpora. Resnik (1992) devel- oped a method for automatically extracting class- based SRs from on-line corpora. Ribas (1994a) *This research has been made in the framework of the Acquilex-II Esprit Project (7315), and has been supported by a grant of Departament d'Ensenyament, Generalitat de Catalunya, 91-DOGC-1491. performed some experiments using this basic tech- nique and drew up some limitations from the cor- responding results. In this paper we will describe some substantial modifications to the basic technique and will re- port the corresponding experimental evaluation. The outline of the paper is as follows: in section 2 we summarize the basic methodology used in (Ribas, 1994a), analyzing its limitations; in sec- tion 3 we explore some alternative statistical mea- sures for ranking the hypothesized SRs; in sec- tion 4 we propose some evaluation measures on the SRs-learning problem, and use them to test the experimental results obtained by the different techniques; finally, in section 5 we draw up the final conclusions and establish future lines of re- search. 2 The basic technique for learning SRs 2.1 Description The technique functionality can be summarized as: Input The training set, i.e. a list of complement co-occurrence triples, (verb- lemma, syntactic-relationship, noun-lemma) extracted from the corpus. Previous knowledge used A semantic hierar- chy (WordNet 1) where words are clustered in semantic classes, and semantic classes are or- ganized hierarchically. Polysemous words are represented as instances of different classes. Output A set of syntac- tic SRs, (verb-lemma, syntactic-relationship, semantic-class, weight). The final …

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