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The problem of prepositional phrase attachment is crucial to various natural language processing tasks and has received wide attention in the literature. In this paper, we propose an algorithm to disambiguate between PP attachment sites. The algorithm uses a combination of supervised and unsupervised learning along with theWordNet information, which is implemented using a back-o model. Our use of the available sources of lexical knowledge base in combination with large un-annotated corpora generalizes the existing algorithms with improved performance. The algorithm achieved average accuracy of 86:68% over three test data sets with 100% recall. It is further extended to deal with the multiple PP attachment problem using the training based on single PP attachment sites and showed improvement over the earlier works on multiple pp attachment.
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Added on June 4, 2012


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  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Akshar Bharati,U Rohini,P Vishnu,S M Bendre,Rajeev Sangal
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