Readings in information retrieval
Readings in information retrieval
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Automatically extracting and representing collocations for language generation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Discovery of numerous specific topics via term co-occurrence analysis
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Information Retrieval queries often result in a large number of documents found to be relevant. These documents are usually sorted by relevance, not by an analysis of what the user meant. If the document collection contains many documents on one of those meanings, it is hard to find other documents. We present a technique called conceptual grouping that automatically distinguishes between different meanings of a user query, given a document collection. By analysing a word co-occurrence network of a text database, we are able to form groups of words related to the query, grouped by semantic coherence. These groups are used to reorganise the results according to what the user has meant by his query. Testing shows that this automated technique can improve precision, help users find what they need more easily and give them a semantic overview of the document collection.