An Expert Lexicon Approach to Identifying English Phrasal Verbs

An Expert Lexicon Approach to Identifying English Phrasal Verbs Wei Li, Xiuhong Zhang, Cheng Niu, Yuankai Jiang, Rohini Srihari Cymfony Inc. 600 Essjay Road Williamsville, NY 14221, USA {wei, xzhang, cniu, yjiang, rohini}@Cymfony.com Abstract Phrasal Verbs are an important feature of the English language. Properly identifying them provides the basis for an English parser to decode the related structures. Phrasal verbs have been a challenge to Natural Language Processing (NLP) because they sit at the borderline between lexicon and syntax. Traditional NLP frameworks that separate the lexicon module from the parser make it difficult to handle this problem properly. This paper presents a finite state approach that integrates a phrasal verb expert lexicon between shallow parsing and deep parsing to handle morpho-syntactic interaction. With precision/recall combined performance benchmarked consistently at 95.8%-97.5%, the Phrasal Verb identification problem has basically been solved with the presented method. 1 Introduction Any natural language processing (NLP) system needs to address the issue of handling multiword expressions, including Phrasal Verbs (PV) [Sag et al. 2002; Breidt et al. 1996]. This paper presents a proven approach to identifying English PVs based on pattern matching using a formalism called Expert Lexicon. Phrasal Verbs are an important feature of the English language since they form about one third of the English verb vocabulary. 1 Properly 1 For the verb vocabulary of our system based on machine-readable dictionaries and two Phrasal Verb dictionaries, phrasal verb entries constitute 33.8% of the entries. recognizing PVs is an important condition for English parsing. Like single-word verbs, each PV has its own lexical features including subcategorization features that determine its structural patterns [Fraser 1976; Bolinger 1971; Pelli 1976; Shaked 1994], e.g., look for has syntactic subcategorization and semantic features similar to those of search; carry…on shares lexical features with continue. Such lexical features can be represented in the PV lexicon in the same way as those for single-word verbs, but a parser can only use them when the PV is identified. Problems like PVs are regarded as ‘a pain in the neck for NLP’ [Sag et al. 2002]. A proper solution to this problem requires tighter interaction between syntax and lexicon than traditionally available [Breidt et al. 1994]. Simple lexical lookup leads to severe degradation in both precision and recall, as our benchmarks show (Section 4). The recall problem is mainly due to separable PVs such as turn…off which allow for syntactic units to be inserted inside the PV compound, e.g., turn it off, turn the radio off. The precision problem is caused by the ambiguous function of the particle. For example, a simple lexical lookup will mistag looked for as a phrasal verb in sentences such as He looked for quite a while but saw nothing. In short, the traditional NLP framework that separates the lexicon module from a parser makes it difficult to handle this problem properly. This paper presents an expert lexicon approach that integrates the lexical module with contextual checking based on shallow parsing results. Extensive blind benchmarking shows that …

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