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Wide Coverage Incremental Parsing by Learning Attachment Preferences

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AI*IA 2001: Advances in Artificial Intelligence (AI*IA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2175))

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Abstract

This paper presents a novel method for wide coverage parsing using an incremental strategy, which is psycholinguistically motivated. A recursive neural network is trained on treebank data to learn first pass attachments, and is employed as a heuristic for guidingpa rsingde cision. The parser is lexically blind and uses beam search to explore the space of plausible partial parses and returns the full analysis havinghi ghest probability. Results are based on preliminary tests on the WSJ section of the Penn treebank and suggest that our incremental strategy is a computationally viable approach to parsing.

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© 2001 Springer-Verlag Berlin Heidelberg

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Costa, F., Lombardo, V., Frasconi, P., Soda, G. (2001). Wide Coverage Incremental Parsing by Learning Attachment Preferences. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_30

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  • DOI: https://doi.org/10.1007/3-540-45411-X_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42601-1

  • Online ISBN: 978-3-540-45411-3

  • eBook Packages: Springer Book Archive

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