Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
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AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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Communications of the ACM
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HLTKM '01 Proceedings of the workshop on Human Language Technology and Knowledge Management - Volume 2001
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NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Domain relevance on term weighting
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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Pattern Recognition Letters
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ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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In large collections of documents that are divided into predefined classes, the differences and similarities of those classes are of special interest. This paper presents an approach that is able to automatically extract terms from such document collections which describe what topics discriminate a single class from the others (discriminating terms) and which topics discriminate a subset of the classes against the remaining ones (overlap terms). The importance for real world applications and the effectiveness of our approach are demonstrated by two out of practice examples. In a first application our predefined classes correspond to different scientific conferences. By extracting terms from collections of papers published on these conferences, we determine automatically the topical differences and similarities of the conferences. In our second application task we extract terms out of a collection of product reviews which show what features reviewers commented on. We get these terms by discriminating the product review class against a suitable counter-balance class. Finally, our method is evaluated comparing it to alternative approaches.