Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Effective ranking with arbitrary passages
Journal of the American Society for Information Science and Technology
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
Two-stage language models for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
A language modeling framework for resource selection and results merging
Proceedings of the eleventh international conference on Information and knowledge management
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Using top-ranking sentences to facilitate effective information access: Book Reviews
Journal of the American Society for Information Science and Technology
Information retrieval system evaluation: effort, sensitivity, and reliability
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty detection based on sentence level patterns
Proceedings of the 14th ACM international conference on Information and knowledge management
Aspects of sentence retrieval
An analysis on document length retrieval trends in language modeling smoothing
Information Retrieval
Assessing multivariate Bernoulli models for information retrieval
ACM Transactions on Information Systems (TOIS)
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling framework for expert finding
Information Processing and Management: an International Journal
Using opinion-based features to boost sentence retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Highly frequent terms and sentence retrieval
SPIRE'07 Proceedings of the 14th international conference on String processing and information retrieval
Exploiting real-time information retrieval in the microblogosphere
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Position-based contextualization for passage retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Employing effective methods of sentence retrieval is essential for many tasks in Information Retrieval, such as summarization, novelty detection and question answering. The best performing sentence retrieval techniques attempt to perform matching directly between the sentences and the query. However, in this paper, we posit that the local context of a sentence can provide crucial additional evidence to further improve sentence retrieval. Using a Language Modeling Framework, we propose a novel reformulation of the sentence retrieval problem that extends previous approaches so that the local context is seamlessly incorporated within the retrieval models. In a series of comprehensive experiments, we show that localized smoothing and the prior importance of a sentence can improve retrieval effectiveness. The proposed models significantly and substantially outperform the state of the art and other competitive sentence retrieval baselines on recall-oriented measures, while remaining competitive on precision-oriented measures. This research demonstrates that local context plays an important role in estimating the relevance of a sentence, and that existing sentence retrieval language models can be extended to utilize this evidence effectively.