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A flexible unsupervised PP-attachment method using semantic information

DSpace at IIT Bombay

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Title A flexible unsupervised PP-attachment method using semantic information
 
Creator MEDIMI, S
BHATTACHARYYA, P
 
Description In this paper we revisit the classical NLP problem of prepositional phrase attachment (PP-attachment). Given the pattern V - NP(1) - P - NP(2) in the text, where V is verb, NP(1) is a noun phrase, P is the preposition and NP(2) is the other noun phrase, the question asked is where does P - NP(2) attach: V or NP(1)? 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 N(1) - P - N(2) tuples of the training corpus TEACH the system how to resolve the attachments in the ambiguous V - N(1) - P - N(2) 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 Vs and Ns in the unambiguous patterns of V - P - NP, NP(1) - P - NP(2). For experimentation, Brown Corpus provides the training data and Wall 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.
 
Publisher IJCAI-INT JOINT CONF ARTIF INTELL
 
Date 2011-10-26T04:25:38Z
2011-12-15T09:11:51Z
2011-10-26T04:25:38Z
2011-12-15T09:11:51Z
2007
 
Type Proceedings Paper
 
Identifier 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-07), PROCEEDINGS,1677-1682
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15909
http://hdl.handle.net/100/2333
 
Source Workshop on Analytics for Noisy Unstructured Text Data held in Conjunction with the 20th International Joint Conference on Artificial Intelligence,Hyderabad, INDIA,JAN 06-12, 2007
 
Language English