A Flexible Unsupervised PP-Attachment Method Using Semantic Information

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) detec...

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Srinivas Medimi, Pushpak Bhattacharyya
• Keywords: Artificial Intelligence, Data Sparsity, IJCAI 2007, Noun Phrase, Word Sense Disambiguation