Summary of the paper

Title Error Analysis for Learning-based Coreference Resolution
Authors Olga Uryupina
Abstract State-of-the-art coreference resolution engines show similar performance figures (low sixties on the MUC-7 data). Our system with a rich linguistically motivated feature set yields significantly better performance values for a variety of machine learners, but still leaves substantial room for improvement. In this paper we address a relatively unexplored area of coreference resolution - we present a detailed error analysis in order to understand the issues raised by corpus-based approaches to coreference resolution.
Language Single language
Topics Anaphora, Coreference, Discourse, Statistical methods
Full paper Error Analysis for Learning-based Coreference Resolution
Slides Error Analysis for Learning-based Coreference Resolution
Bibtex @InProceedings{URYUPINA08.487,
  author = {Olga Uryupina},
  title = {Error Analysis for Learning-based Coreference Resolution},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {},
  language = {english}

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