Name
Abstract | ||
|---|---|---|
Converting questions to effective queries is crucial to open-domain question answering systems. In this paper, we present a web-based unsupervised learning approach for transforming a given natural-language question to an effective query. The method involves querying a search engine for Web passages that contain the answer to the question, extracting patterns that characterize fine-grained classification for answers, and linking these patterns with n-grams in answer passages. Independent evaluation on a set of questions shows that the proposed approach outperforms a naive keyword-based approach in terms of mean reciprocal rank and human effort. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11562214_46 | IJCNLP |
Keywords | Field | DocType |
web-based unsupervised learning approach,effective query,query formulation,fine-grained classification,web-based unsupervised learning,web passage,natural-language question,naive keyword-based approach,converting question,question answering system,answer passage,unsupervised learning,question answering | Web search query,Question answering,Query expansion,Computer science,Web query classification,Unsupervised learning,Natural language,Mean reciprocal rank,Artificial intelligence,Natural language processing,Web application | Conference |
Volume | ISSN | ISBN |
3651 | 0302-9743 | 3-540-29172-5 |
Citations | PageRank | References |
1 | 0.37 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Chia Wang | 1 | 412 | 28.15 |
| Jian-Cheng Wu | 2 | 70 | 13.30 |
| Tyne Liang | 3 | 327 | 28.81 |
| Jason S. Chang | 4 | 345 | 62.64 |