A System for Large-Scale Acquisition of Verbal, Nominal and Adjectival Subcategorization Frames from Corpora

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 912–919, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics A System for Large-Scale Acquisition of Verbal, Nominal and Adjectival Subcategorization Frames from Corpora Judita Preiss, Ted Briscoe, and Anna Korhonen Computer Laboratory University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 0FD, UK Judita.Preiss, Ted.Briscoe, Anna.Korhonen@cl.cam.ac.uk Abstract This paper describes the first system for large-scale acquisition of subcategorization frames (SCFs) from English corpus data which can be used to acquire comprehen- sive lexicons for verbs, nouns and adjectives. The system incorporates an extensive rule- based classifier which identifies 168 verbal, 37 adjectival and 31 nominal frames from grammatical relations (GRs) output by a ro- bust parser. The system achieves state-of- the-art performance on all three sets. 1 Introduction Research into automatic acquisition of lexical in- formation from large repositories of unannotated text (such as the web, corpora of published text, etc.) is starting to produce large scale lexical re- sources which include frequency and usage infor- mation tuned to genres and sublanguages. Such resources are critical for natural language process- ing (NLP), both for enhancing the performance of state-of-art statistical systems and for improving the portability of these systems between domains. One type of lexical information with particular importance for NLP is subcategorization. Access to an accurate and comprehensive subcategoriza- tion lexicon is vital for the development of success- ful parsing technology (e.g. (Carroll et al., 1998), important for many NLP tasks (e.g. automatic verb classification (Schulte im Walde and Brew, 2002)) and useful for any application which can benefit from information about predicate-argument struc- ture (e.g. Information Extraction (IE) ((Surdeanu et al., 2003)). The first systems capable of automatically learn- ing a small number of verbal subcategorization frames (SCFs) from unannotated English corpora emerged over a decade ago (Brent, 1991; Manning, 1993). Subsequent research has yielded systems for English (Carroll and Rooth, 1998; Briscoe and Car- roll, 1997; Korhonen, 2002) capable of detecting comprehensive sets of SCFs with promising accu- racy and demonstrated success in application tasks (e.g. (Carroll et al., 1998; Korhonen et al., 2003)). Recently, a large publicly available subcategoriza- tion lexicon was produced using such technology which contains frame and frequency information for over 6,300 English verbs – the VALEX lexicon (Ko- rhonen et al., 2006). While there has been considerable work in the area, most of it has focussed on verbs. Although verbs are the richest words in terms of subcatego- rization and although verb SCF distribution data is likely to offer the greatest boost in parser perfor- mance, accurate and comprehensive knowledge of the many noun and adjective SCFs in English could improve the accuracy of parsing at several levels (from tagging to syntactic and semantic analysis). Furthermore the selection of the correct analysis from the set returned by a parser which does not ini- tially utilize fine-grained lexico-syntactic informa- tion can depend on the interaction of conditional probabilities of lemmas of different classes occur- 912 ring with specific …

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