Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring the similarity space
ACM SIGIR Forum
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Effective document presentation with a locality-based similarity heuristic
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Efficient passage ranking for document databases
ACM Transactions on Information Systems (TOIS)
A comparison of various approaches for using probabilistic dependencies in language modeling
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Passage retrieval vs. document retrieval for factoid question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Probabilistic document-context based relevance feedback with limited relevance judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Query-specific clustering of search results based on document-context similarity scores
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A retrospective study of a hybrid document-context based retrieval model
Information Processing and Management: an International Journal
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
An information retrieval approach based on discourse type
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
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We propose a novel probabilistic retrieval model which weights terms according to their contexts in documents. The term weighting function of our model is similar to the language model and the binary independence model. The retrospective experiments (i.e., relevance information is present) illustrate the potential of our probabilistic context-based retrieval where the precision at the top 30 documents is about 43% for TREC-6 data and 52% for TREC-7 data.