Learning Transformation Rules to Find Grammatical Relations

Learning Transformation Rules to Find Grammatical Relations* Lisa Ferro and Marc Vilain and Alexander Yeh Abstract Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these relationships through transfor- mation sequences and-error-driven learning. Our ap- proach finds grammatical relationships between core syntax groups and bypasses much of the parsing phase. On our training and test set, our procedure achieves 63.6% recall and 77.3% precision (f-score = 69.8). Introduction An important level of natural language process- ing is the finding of grammatical relationships such as subject, object, modifier, etc. Such relation- ships are the objects of study in relational grammar [Perlmutter, 1983]. Many systems (e.g., the KERNEL system [Palmer et al., 1993]) use these relationships as an intermediate, form when determining the semantics of syntactically parsed text. In the SPARKLE project [Carroll et al., 1997@ grammatical relations form the layer above the phrasal-level in a three layer syntax scheme. Grammatical relationships are often stored in some type of structure like the F-structures of lexical- functional grammar [Kaplan, 1994]. Our own interest in grammatical relations is as a se- mantic basis for information extraction in the Alembic system. The extraction approach we are currently in- vestigating exploits grammatical relations as an inter- mediary between surface syntactic phrases and proposi- tional semantic interpretations. By directly associating syntactic heads with their arguments and modifiers, we are hoping that these grammatical relations will provide a high degree of generality and reliability to the process of composing semantic representations. This ability to • This paper reports on work performed at the MITRE Corporation under the support of the MITRE Sponsored Research Program. Helpful assistance has been given by" Yuval Krymolowski, Lynette Hirschman and an anonymous reviewer. Copyright (~)1999 The MITRE Corporation. All rights reserved. The MITRE Corporation 202 Burlington Rd. Bedford, MA 01730 USA {lferro,mbv,asy}@mitre.org "parse" into a semantic representation is according to Charniak [Charniak, 1997, p. 42], "the most important task to be tackled now." In this paper, we describe a system to learn rules for finding grammatical relationships when just given a partial parse with entities like names, core noun m~d verb phrases (noun and verb groups) and semi-accurate estimates of the attachments of prepositions and subor- dinate conjunctions. In our system, the different enti- ties, attachments and relationships are found using rule sequence processors that are cascaded together. Each processor can be thought of as approximating some as- pect of the underlying grammar by finite-state trans- duction. We present the problem scope of interest to us, as well as the data annotations required to support our investi- gation. We also present a decision procedure for finding grammatical relationships. In brief, on our training mid test set, our procedure achieves 63.6% recall and 77.3% precision, for an f-score of 69.8. Phrase Structure and Grammatical Relations In standard derivational approaches to syntax, start- ing as early as 1965 [Chomsky, 1965], the notion of grammatical relationship is typically parasitic on that of phrase structure. That is to say, …

Content truncated to 500 words