Automatically Categorizing Written Texts by Author Gender.
The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and...
| Publicado en: | Literary & Linguistic Computing Vol. 17; no. 4; pp. 401 - 412 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Nov2002
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80 per cent accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98 per cent accuracy. |
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