Guiding a Well-Founded Parser with Corpus Statistics

Guiding a Well-Founded Parser with Corpus Statistics Amon Seagull and Lenhart Schubert Department of Computer Science University of Rochester Rochester, NY 14627 {seagull, schubert }@cs. rochester, edu Abstract We present a parsing system built from a hand- written lexicon ~ and grammar, and trained on a selection of the Brown Corpus. On the sen- tences it can parse, the parser performs as well as purely corpus-based parsers. Its advantage lies in the fact that its syntactic analyses read- ily support semantic interpretation. Moreover, the system's hand-written foundation allows for a more fully lexicalized probabilistic model, i.e. one sensitive to co-occurrence of lexical heads of phrase constituents. 1 Introduction Statistical approaches to parsing have received a great deal of attention over recent years. The availability of large tagged and syntac- tically bracketed corpora make the program- matic extraction of lexica and grammars fea- sible. Researchers have tackled parsing by sub- stituting these automatically derived resources for hand-coded ones. While these approaches have had som e success to date (Collins, 1997; Charniak, 1997a), their usability as parsers in systems for natural language understanding is suspect. 1 The 'reconstruction of Treebank-style bracketings does not serve as an adequate basis for semantic interpretation. The phrase struc- ture rules are too numerous, and the analy- ses too coarse (especially at the lower levels) to allow association of deterministic semantic rules with ph~:ase structure rules. Chaxniak himself (1997b) notes that most of the parses constructed by a "wide-coverage" grammar axe "pretty senseless". 1Collins, Ch~niak, etc. make no claims about their programs being Well suited as parsers for language un- derstanding applications. Certainly, this type of parsing has had success t'o-date in applications such as Informa- tion Retrieval. As an example, consider the fiat NP structures that are in the Penn Treebank (Marcus et al., 1993). Nouns, determiners, and adjectives are all sisters of each other in the syntactic anno- tation, e.g. (NP (DT the) (JJ mechanical) (NN engineering) (NN industry)). A parser which constructs structures such as this fails to solve an ambiguity problem that has generally been considered syntactic: Are we talking about the industry of mechanical engineering, or is the entire engineering industry perceived as me- chanical? If our goal is language understanding, including semantic interpretation, the Treebank bracketings must be considered underspecified. We describe here a system which combines hand-coded linguistic resources with corpus- derived probabilistic information to enable (fairly) wide-coverage syntactic parsing. Most importantly, the use of these linguistic resources allows for a better-informed probabilistic model. 2 Setup Our lexicon is composed from two resources. COMLEX (Grishman et al., 1994) provides the syntactic and morphological information for 39,000 lemmas. WordNet (Fellbaum, 1998) pro- vides the semantic information. In addition, we add to our lexicon approximately 47,000 "multi- word" nouns found in WordNet. SAPIR, the parser we are using, employs a feature-based general grammar of English that has been in development at The Boeing Com- pany over the past fifteen years (Harrison and Maxwell, 1986). The grammar consists of ap- proximately 500 rules. …

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