A Sequential Algorithm for Training Text Classifiers

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A Sequential Algorithm for Training Text Classifiers (1994)

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by David D. Lewis , William A. Gale

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631 - 10 self

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@INPROCEEDINGS{Lewis94asequential,
    author = {David D. Lewis and William A. Gale},
    title = {A Sequential Algorithm for Training Text Classifiers},
    booktitle = {},
    year = {1994},
    pages = {3--12},
    publisher = {Springer-Verlag}
}

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Abstract

The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness. 1 Introduction Text classification is the automated grouping of textual or partially textual entities. Document retrieval, categorization, routing, filtering, and clustering, as well as natural language processing tasks such as tagging, word sense disambiguation, and some aspects of understanding can be formulated as text classification. As the amount of online text increases, the demand for text classification to aid the analysis and mana...

Keyphrases

training text classifier sequential algorithm text classification training data online text increase natural language sequential sampling word sense disambiguation document retrieval machine learning content analysis information retrieval uncertainty sampling natural language processing automated grouping statistical classifier train text classifier textual entity newswire text categorization task introduction text classification

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