The use of phrases and structured queries in information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGIR '93 Proceedings of the 16th 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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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Word sense disambiguation in information retrieval revisited
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
Semantic cores for representing documents in IR
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Semantic indexing using WordNet senses
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using measures of semantic relatedness for word sense disambiguation
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This paper addresses an important problem related to the use of semantics in IR. It concerns the representation of document semantics and its proper use in retrieval. The approach we propose aims at representing the content of the document by the best semantic network called document semantic core in two main steps. During the first step concepts (words and phrases) are extracted from a document, driven by an external general-purpose ontology, namely WordNet. The second step a global disambiguation of the extracted concepts regarding to the document leads to build the best semantic network. Thus, the selected concepts represent the nodes of the semantic network whereas similarity measure values between connected nodes weight the links. The resulting scored concepts are used for the document conceptual indexing in Information Retrieval.