A Joint Model for Quotation Attribution and Coreference Resolution
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Table 5 presents the impact of aggregating feature sets on the performance of our pairwise event coreference model using the ground truth event mentions (coreference threshold
To answer this question, we show in row 7 of Table 1 the results ob- tained using the pipeline architecture, where (1) an anaphoricity classifier is trained with all the features
More specifically, to combine mention cluster- ing with pairwise classification, we adopt the com- monly used strategies (such as best-first clustering and transitivity constraint),
The obtained mention groups represent the final equivalence classes of co-referring mentions across documents – capturing both in-KB entities (with links to the KB) in the SE class
In this paper a different machine learning approach to deal with the coreference resolution task is presented: a multi-classifier system that classifies mention-pairs in a