A Flexible Unsupervised PP-Attachment Method Using Semantic Information

Abstract

In this paper we revisit the classical NLP problem of prepositional phrase attachment (PPattachment). Given the pattern V −NP1−P−NP2 in the text, where V is verb, NP1 is a noun phrase, P is the preposition and NP2 is the other noun phrase, the question asked is where does P − NP2 attach: V or NP1? This question is typically answered using both the word and the world knowledge. Word Sense Disambiguation (WSD) and Data Sparsity Reduction (DSR) are the two requirements for PP-attachment resolution. Our approach described in this paper makes use of training data extracted from raw text, which makes it an unsupervised approach. The unambiguous V −P −N and N1 −P −N2 tuples of the training corpus TEACH the system how to resolve the attachments in the ambiguous V − N1 − P − N2 tuples of the test corpus. A graph based approach to word sense disambiguation (WSD) is used to obtain the accurate word knowledge. Further, the data sparsity problem is addressed by (i) detecting synonymy using the wordnet and (ii) doing a form of inferencing based on the matching of V s and Ns in the unambiguous patterns of V −P −NP, NP1−P −NP2. For experimentation, Brown Corpus provides the training data andWall Street Journal Corpus the test data. The accuracy obtained for PP-attachment resolution is close to 85%. The novelty of the system lies in the flexible use of WSD and DSR phases.