An algorithm for pronominal anaphora resolution
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We present a novel approach for boosting the performance of pronominal anaphora resolution algorithms when search for antecedents has to span over a multi-sentential text passage. The approach is based on the identification of sentences which are “most semantically related” to the sentence with anaphora. The context sharing level between each possible referent sentence and the anaphoric sentence gets established utilizing an open-domain external knowledge base. Sentences with scores higher than a threshold level are considered the “most semantically related” and ranked accordingly. The qualified sentences accompanied with their context sharing scores represent a new, reduced in size, and a more semantically focused search space. Their respective scores are utilized as separate preference factors in a final phase of the resolution process – the antecedent selection. We pioneer three implementations for the algorithm with their corresponding evaluation data.