Summary of the paper

Title Answering List Questions using Co-occurrence and Clustering
Authors Majid Razmara and Leila Kosseim
Abstract Although answering list questions is not a new research area, answering them automatically still remains a challenge. The median F-score of systems that participated in TREC 2007 Question Answering track is still very low (0.085) while 74% of the questions had a median F-score of 0. In this paper, we propose a novel approach to answering list questions. This approach is based on the hypothesis that answer instances of a list question co-occur in the documents and sentences related to the topic of the question. We use a clustering method to group the candidate answers that co-occur more often. To pinpoint the right cluster, we use the target and the question keywords as spies to return the cluster that contains these keywords.
Language Language-independent
Topics Question Answering, Statistical methods, Information Extraction, Information Retrieval
Full paper Answering List Questions using Co-occurrence and Clustering
Slides Answering List Questions using Co-occurrence and Clustering
Bibtex @InProceedings{RAZMARA08.814,
  author = {Majid Razmara and Leila Kosseim},
  title = {Answering List Questions using Co-occurrence and Clustering},
  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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