Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Memory-based learning: using similarity for smoothing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Combining unsupervised and supervised methods for PP attachment disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
A nearest-neighbor method for resolving PP-Attachment ambiguity
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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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 the WordNet information, which is implemented using a back-off 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.