Abstract
Extracting information automatically from texts for database representation requires previously well-grouped phrases so that entities can be separated adequately. This problem is known as prepositional phrase (PP) attachment disambiguation. Current PP attachment disambiguation systems require an annotated treebank or they use an Internet connection to achieve a precision of more than 90. Unfortunately, these resources are not always available. In addition, using the same techniques that use the Web as corpus may not achieve the same results when using local corpora. In this paper, we present an unsupervised method for generalizing local corpora information by means of semantic classification of nouns based on the top 25 unique beginner concepts of WordNet. Then we propose a method for using this information for PP attachment disambiguation.
Work done under partial support of Mexican Government (CONACyT, SNI, PIFI-IPN, CGEPI-IPN), Korean Government (KIPA), ITRI of Chung-Ang University, and RITOS-2. The second author is currently on Sabbatical leave at Chung-Ang University.
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Calvo, H., Gelbukh, A. (2004). Acquiring Selectional Preferences from Untagged Text for Prepositional Phrase Attachment Disambiguation. In: Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2004. Lecture Notes in Computer Science, vol 3136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27779-8_18
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DOI: https://doi.org/10.1007/978-3-540-27779-8_18
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