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