Automatic Acquisition of Adjectival Subcategorization from Corpora

Proceedings of the 43rd Annual Meeting of the ACL, pages 614–621, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Automatic Acquisition of Adjectival Subcategorization from Corpora Jeremy Yallop∗, Anna Korhonen, and Ted Briscoe Computer Laboratory University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 OFD, UK yallop@cantab.net, {Anna.Korhonen, Ted.Briscoe}@cl.cam.ac.uk Abstract This paper describes a novel system for acquiring adjectival subcategorization frames (SCFs) and associated frequency information from English corpus data. The system incorporates a decision-tree classifier for 30 SCF types which tests for the presence of grammatical relations (GRs) in the output of a robust statisti- cal parser. It uses a powerful pattern- matching language to classify GRs into frames hierarchically in a way that mirrors inheritance-based lexica. The experiments show that the system is able to detect SCF types with 70% precision and 66% recall rate. A new tool for linguistic annotation of SCFs in corpus data is also introduced which can considerably alleviate the pro- cess of obtaining training and test data for subcategorization acquisition. 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 ∗Part of this research was conducted while this author was at the University of Edinburgh Laboratory for Foundations of Computer Science. 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., 1998b), 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 English corpora emerged over a decade ago (Brent, 1991; Manning, 1993). Subse- quent research has yielded systems for English (Car- roll and Rooth, 1998; Briscoe and Carroll, 1997; Ko- rhonen, 2002) capable of detecting comprehensive sets of SCFs with promising accuracy and demon- strated success in application tasks (e.g. (Carroll et al., 1998b; Korhonen et al., 2003)), besides systems for a number of other languages (e.g. (Kawahara and Kurohashi, 2002; Ferrer, 2004)). While there has been considerable research into acquisition of verb subcategorization, we are not aware of any systems built for adjectives. Al- though adjectives are syntactically less multivalent than verbs, and although verb subcategorization dis- tribution data appears to offer the greatest potential boost in parser performance, accurate and compre- hensive knowledge of the many adjective SCFs can improve the accuracy of parsing at several levels 614 (from tagging to syntactic and semantic analysis). …

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