Design of a browsing interface for information retrieval
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
International Journal of Man-Machine Studies
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Cross-Language Information Access through Phrase Browsing
NLDB'01 Proceedings of the 6th International Workshop on Applications of Natural Language to Information Systems
Terminology Retrieval: Towards a Synergy between Thesaurus and Free Text Searching
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
A Document Descriptor Extractor Based on Relevant Expressions
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Towards automatic building of document keywords
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Evaluating hierarchical clustering of search results
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
Automatically adapting the context of an intranet query
FDIA'08 Proceedings of the 2nd BCS IRSG conference on Future Directions in Information Access
Text mining scientific papers: a survey on FCA-Based information retrieval research
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
A general evaluation measure for document organization tasks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Automatic attribute selection is a critical step when using Formal Concept Analysis (FCA) in a free text document retrieval framework. Optimal attributes as document descriptors should produce smaller, clearer and more browsable concept lattices with better clustering features. In this paper we focus on the automatic selection of noun phrases as document descriptors to build an FCA-based IR framework. We present three different phrase selection strategies which are evaluated using the Lattice Distillation Factor and the Minimal Browsing Area evaluation measures. Noun phrases are shown to produce lattices with good clustering properties, with the advantage (over simple terms) of being better intensional descriptors from the user's point of view.