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 = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |