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Exploiting Semantic Information for HPSG Parse Selection

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Research on Language and Computation


In this article, we investigate the use of semantic information in parse selection. We show that fully disambiguated sense-based semantic features smoothed using ontological information are effective for parse selection. Training and testing was undertaken using definition and example sentences taken from a Japanese dictionary corpus (Hinoki), which is manually annotated with senses. A model employing both syntactic and semantic information provides better parse selection accuracy than a model using only syntactic features.

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Correspondence to Sanae Fujita.

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This article presents an updated and extended version of results first published by Fujita et al. (2007).

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Fujita, S., Bond, F., Oepen, S. et al. Exploiting Semantic Information for HPSG Parse Selection. Res on Lang and Comput 8, 1–22 (2010).

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