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Building a Taxonomy for Auto-classification.

Taxonomies have expanded from browsing aids to the foundation for automatic classification. Early auto-classification methods grouped documents having similar collections of words, but current software can provide far greater accuracy. Text-mining tools can spot recognizable entities as potential ta...

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Published in:Bulletin of the American Society for Information Science & Technology Vol. 39; no. 2; pp. 34 - 39
Main Author: Pohs, Wendi
Format: pictorial Journal Article
Published: Wiley-Blackwell Dec2012/Jan2013
Online Access:View this record in EBSCOhost
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      dt: Dec2012/Jan2013
      vid: 39
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1002/bult.2013.1720390210
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        atl: Building a Taxonomy for Auto-classification.
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        au: Pohs, Wendi
        affil: Principal, InfoClear Consulting
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        subj:
          Vocabulary, Controlled
          Classification Methods
          Software
          Information Management
      ab: Taxonomies have expanded from browsing aids to the foundation for automatic classification. Early auto-classification methods grouped documents having similar collections of words, but current software can provide far greater accuracy. Text-mining tools can spot recognizable entities as potential taxonomy terms or top-level categories. Developing a taxonomy further to coordinate with auto-classification software requires appreciation of how the software works, whether it uses an approach based on lexical analysis, rules for word co-occurrence or machine learning with predictive analysis. The taxonomy model is typically hierarchical with term specificity dictated by the end user's need for detail. Synonyms and variants are redirected to the term for classification. The classification tool must be configured to be consistent with the typical document format and style of the collection. Testing the classification scheme, critical to reveal inaccuracies and omissions, is an iterative process expanding from a stable test set to validation on a large corpus before final implementation.
      pubtype: Academic Journal
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        Journal Article
      ougenre: Article
    language: English
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