Latent Structures for Coreference Resolution
Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.
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BIG-bench Machine Learningcoreference-resolutionCoreference ResolutionMachine TranslationStructured PredictionSimilar Papers 제목 키워드 기반
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