Summary of the paper

Title Towards Multiple Antecedent Coreference Resolution in Specialized Discourse
Authors Alicia Burga, Sergio Cajal, Joan Codina-Filba and Leo Wanner
Abstract Despite the popularity of coreference resolution as a research topic, the overwhelming majority of the work in this area focused so far on single antecedence coreference only. Multiple antecedent coreference (MAC) has been largely neglected. This can be explained by the scarcity of the phenomenon of MAC in generic discourse. However, in specialized discourse such as patents, MAC is very dominant. It seems thus unavoidable to address the problem of MAC resolution in the context of tasks related to automatic patent material processing, among them abstractive summarization, deep parsing of patents, construction of concept maps of the inventions, etc. We present the first version of an operational rule-based MAC resolution strategy for patent material that covers the three major types of MAC: (i) nominal MAC, (ii) MAC with personal / relative pronouns, and MAC with reflexive / reciprocal pronouns. The evaluation shows that our strategy performs well in terms of precision and recall.
Topics Anaphora, Coreference, Tools, Systems, Applications, Knowledge Discovery/Representation
Full paper Towards Multiple Antecedent Coreference Resolution in Specialized Discourse
Bibtex @InProceedings{BURGA16.812,
  author = {Alicia Burga and Sergio Cajal and Joan Codina-Filba and Leo Wanner},
  title = {Towards Multiple Antecedent Coreference Resolution in Specialized Discourse},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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