Word sense disambiguation and information retrieval
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Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
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Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
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IEEE Transactions on Knowledge and Data Engineering
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CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
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ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
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ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Context-sensitive semantic smoothing for the language modeling approach to genomic IR
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
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In this paper, we explore the direct use of relations in information retrieval for precision-focused biomedical literature search. A relation is defined as a pair of two concepts which are semantically and syntactically related to each other. Unlike the traditional term-based IR models, our model represents a document by a set of controlled concepts and their binary relations. Since document level co-occurrence of two concepts, in many cases, does not mean this document really addresses their relationships, the direct use of relation may improve the precision of very specific search, e.g. searching documents that mention genes regulated by Smad4. For this purpose, we develop a generic ontology-based approach to extract concepts and their relations; a prototyped IR system supporting relation-based search is then built for Medline abstract search. We then use this novel IR system to improve the retrieval result of all official runs in TREC-2004 Genomics Track. The experiment shows promising performance of relation-based IR. The mean of P@100 (the precision of top 100 documents) for all 50 topics is raised from 26.37 %( the P@100 of the best run is 42.10%) to 53.69% while the recall is kept at an acceptable level of 44.31%. The experiment also demonstrates the expressiveness of relations for the representation of genomic information needs.