Abstract
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.
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Bharathi, A., Rohini, U., Vishnu, P., Bendre, S.M., Sangal, R. (2005). A Hybrid Approach to Single and Multiple PP Attachment Using WordNet. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_19
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DOI: https://doi.org/10.1007/11562214_19
Publisher Name: Springer, Berlin, Heidelberg
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