Titlebook: Natural Language Information Retrieval; Tomek Strzalkowski Book 1999 Springer Science+Business Media Dordrecht 1999 DOM.Syntax.classificat

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Combining Corpus Linguistics and Human Memory Models for Automatic Term Association,rms. A human memory model is modified in such a way that it produces additional search terms instead of human associations. A small experiment shows that such a spreading activation network can find alternative terms — with a performance similar to the normally used similarity measures.

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Document Classification and Routing,bilities are determined from the relevant training documents. Development, refinement, and testing of the system’s ability to route 120,000 documents into 50 topics are discussed as well as the mathematical model on which it is based.

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Murax: Finding and Organizing Answers from Text Search, different tack, directed to high-precision retrieval and an explicit organization of answer text which may be assembled from several different documents. For this, shallow linguistic analysis is deployed in a manner such that the robustness afforded by traditional retrieval techniques is maintained.

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The Use of Categories and Clusters for Organizing Retrieval Results,t categorization and text clustering are two natural language processing tasks whose results can be applied to document organization. This chapter describes user interfaces that use categories and clusters to organize retrieval results, and examines the relationship between the two..

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Natural Language Information Retrieval978-94-017-2388-6Series ISSN 1386-291X Series E-ISSN 2542-9388

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Extraction-Based Text Categorization: Generating Domain-Specific Role Relationships Automatically, be generated automatically using only preclassified texts as input. Second, we present the . algorithm that uses lexical items to represent domain-specific role relationships instead of semantic features. Using these techniques, we can automatically build text categorization systems that benefit from domain-specific natural language processing.