• Corpus ID: 618047

Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task

@inproceedings{Lee2011StanfordsMS,
  title={Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task},
  author={Heeyoung Lee and Yves Peirsman and Angel X. Chang and Nathanael Chambers and Mihai Surdeanu and Dan Jurafsky},
  booktitle={CoNLL Shared Task},
  year={2011}
}
This paper details the coreference resolution system submitted by Stanford at the CoNLL-2011 shared task. [] Key Result Our system was ranked first in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track.

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This generative, model-based approach in which each of these factors is modularly encapsulated and learned in a primarily unsu-pervised manner is presented, resulting in the best results to date on the complete end-to-end coreference task.

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An insight is provided into (i) the portability of coreference resolution systems across languages, and (ii) the effect of different scoring metrics on ranking the output of the participant systems.

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We investigate methods to improve the recall in coreference resolution by also trying to resolve those definite descriptions where no earlier mention of the referent shares the same lexical head
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