Year: 2015
Type: article
Abstract: 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.
DOI: 10.1162/tacl_a_00147
Authors: Sebastian Martschat, Michael Strube
Institution: Heidelberg Institute for Theoretical Studies
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Klaus Tschira Stiftung
OpenAlex ID: W2180160918