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Semantic Inference for Anaphora Resolution: Toward a Framework in Machine Translation

Samuel W. K. Chan and Benjamin K. T'Sou
Machine Translation
Vol. 14, No. 3/4, Anaphora Resolution in Machine Translation (1999), pp. 163-190
Published by: Springer
Stable URL: http://www.jstor.org/stable/40006920
Page Count: 28
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Semantic Inference for Anaphora Resolution: Toward a Framework in Machine Translation
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Abstract

Anaphora is a discourse-level linguistic phenomenon. There is consensus that anaphora resolution should rely on prior sentences within the context of the discourse. We propose to cast anaphora resolution as a semantic inference process in which a combination of multiple strategies, each exploiting different aspects of linguistic knowledge, is employed to provide a coherent resolution of anaphora. A framework which encompasses several salient linguistic parameters such as grammatical role, proximity, repetition, sentence recency and semantic cues is demonstrated. This work also shows how an anaphora-resolution algorithm can be embedded within a framework which captures all the above salient parameters, as well as remedies some of the inadequacies found in any monolithic resolution system. A language-neutral semantic representation characterized by semantic cues is presented in order to capture the distilled information after resolution. The effectiveness of the language-neutral representation, both for machine translation and anaphora resolution, is demonstrated through a set of simulations and evaluations.

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