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IJCAI
2007

A Flexible Unsupervised PP-Attachment Method Using Semantic Information

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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...
Srinivas Medimi, Pushpak Bhattacharyya
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Srinivas Medimi, Pushpak Bhattacharyya
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