Error-Driven Pruning of Treebank Grammars for Base Noun Phrase Identification

Error-Driven Pruning of Treebank Grammars for Base Noun Phrase Identification Claire Cardie and David Pierce Department of Computer Science Cornell University Ithaca, NY 14853 cardie, pierce@cs.cornell.edu Abstract Finding simple, non-recursive, base noun phrases is an important subtask for many natural language processing applications. While previous empirical methods for base NP identification have been rather complex, this paper instead proposes a very simple algorithm that is tailored to the relative simplicity of the task. In particular, we present a corpus-based approach for finding base NPs by matching part-of- speech tag sequences. The training phase of the al- gorithm is based on two successful techniques: first the base NP grammar is read from a "treebank" cor- pus; then the grammar is improved by selecting rules with high "benefit" scores. Using this simple algo- rithm with a naive heuristic for matching rules, we achieve surprising accuracy in an evaluation on the Penn Treebank Wall Street Journal. 1 Introduction Finding base noun phrases is a sensible first step for many natural language processing (NLP) tasks: Accurate identification of base noun phrases is ar- guably the most critical component of any partial parser; in addition, information retrieval systems rely on base noun phrases as the main source of multi-word indexing terms; furthermore, the psy- cholinguistic studies of Gee and Grosjean (1983) in- dicate that text chunks like base noun phrases play an important role in human language processing. In this work we define base NPs to be simple, nonre- cursive noun phrases -- noun phrases that do not contain other noun phrase descendants. The brack- eted portions of Figure 1, for example, show the base NPs in one sentence from the Penn Treebank Wall Street Journal (WSJ) corpus (Marcus et al., 1993). Thus, the string the sunny confines of resort towns like Boca Raton and Hot Springs is too complex to be a base NP; instead, it contains four simpler noun phrases, each of which is considered a base NP: the sunny confines, resort towns, Boca Raton, and Hot Springs. Previous empirical research has addressed the problem of base NP identification. Several algo- rithms identify "terminological phrases" -- certain When [it] is [time] for [their biannual powwow] , [the nation] 's [manufacturing titans] typically jet off to [the sunny confines] of [resort towns] like [Boca Raton] and [Hot Springs]. Figure 1: Base NP Examples base noun phrases with initial determiners and mod- ifiers removed: Justeson & Katz (1995) look for repeated phrases; Bourigault (1992) uses a hand- crafted noun phrase grammar in conjunction with heuristics for finding maximal length noun phrases; Voutilainen's NPTool (1993) uses a handcrafted lex- icon and constraint grammar to find terminological noun phrases that include phrase-final prepositional phrases. Church's PARTS program (1988), on the other hand, uses a probabilistic model automati- cally trained on the Brown corpus to locate core noun phrases as well as to assign parts of speech. More recently, Ramshaw & Marcus (In press) ap- ply transformation-based learning (Brill, 1995) to the problem. Unfortunately, it is difficult to …

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