Name
Abstract | ||
|---|---|---|
Sentence retrieval is to retrieve query-relevant sentences in response to user query. However, limited information contained in sentence always incurs a lot of uncertainties, which heavily influence the retrieval performance. To solve this problem, Bayesian network, which has been accepted as one of the most promising methodologies to deal with information uncertainty, is explored. Correspondingly, three sentence retrieval models based on Bayesian network are proposed, i.e. BNSR model, BNSR_TR model and BNSR_CR model. BNSR model assumes independency between terms and shows certain improvement in retrieval performance. BNSR_TR and BNSR_CR models relax the assumption of term independency but consider term relationships from two different points of view, namely term and term context. Experiments verify the performance improvements produced by these two models, but BNSR_CR shows more advantages than BNSR_TR model, because of its more accurate identification of term dependency. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.patrec.2008.06.005 | Pattern Recognition Letters |
Keywords | Field | DocType |
bnsr_tr model,association rule mining,bayesian network,term dependency,sentence retrieval,sentence retrieval model,term relationship,bnsr model,term context,retrieval performance,term independency,bnsr_cr model | Pattern recognition,Computer science,Phrase,Bayesian network,Association rule learning,Information extraction,Natural language processing,Artificial intelligence,Term Discrimination,Sentence,Machine learning | Journal |
Volume | Issue | ISSN |
30 | 9 | Pattern Recognition Letters |
Citations | PageRank | References |
2 | 0.38 | 15 |
Authors | ||
3 | ||