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.

• Publication year

  2015

• Venue

  Transactions of the Association for Computational Linguistics

• Publication date

  2015-07-22

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.1162/tacl_a_00147
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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