Summary of the paper

Title Scaling Answer Type Detection to Large Hierarchies
Authors Kirk Roberts and Andrew Hickl
Abstract This paper describes the creation of a state-of-the-art answer type detection system capable of recognizing more than 200 different expected answer types with greater than 85% precision and recall. After describing how we constructed a new, multi-tiered answer type hierarchy from the set of entity types recognized by Language Computer Corporation’s CICEROLITE named entity recognition system, we describe how we used this hierarchy to annotate a new corpus of more than 10,000 English factoid questions. We show how an answer type detection system trained on this corpus can be used to enhance the accuracy of a state-of-the-art question-answering system (Hickl et al., 2007; Hickl et al., 2006b) by more than 7% overall.
Language Language-independent
Topics Question Answering, Document Classification, Text categorisation, Corpus (creation, annotation, etc.)
Full paper Scaling Answer Type Detection to Large Hierarchies
Slides Scaling Answer Type Detection to Large Hierarchies
Bibtex @InProceedings{ROBERTS08.384,
  author = {Kirk Roberts and Andrew Hickl},
  title = {Scaling Answer Type Detection to Large Hierarchies},
  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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