Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Automatic structuring and retrieval of large text files
Communications of the ACM
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Syntactic features in question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using top-ranking sentences to facilitate effective information access: Book Reviews
Journal of the American Society for Information Science and Technology
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Novelty detection based on sentence level patterns
Proceedings of the 14th ACM international conference on Information and knowledge management
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A translation model for sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Association rule mining: models and algorithms
Association rule mining: models and algorithms
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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.