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Generic natural language distance via online semantic volumetric inference

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Harvard Dataverse2022-11-18 更新2026-04-09 收录
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https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/WKLWF8
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This paper discusses the approach of creating semantic meaning ad hoc through direct explicit volumetric adherence or relative intersection, from online databases, such as Wikipedia or Google. We demonstrate this approach through use of correlation, between a dictionary index – a lexicon - and an import/export industry ISO A129 standard used by the Ministry of Finances, in the French language. We conclude, this approach by giving the most and least meaningful industrial results, for the French language. This questions whereas online apparent generic Natural language processing (NLP) pivot Chomsky Universal grammar (UG) representation, could inherit implicit initial national culture.
提供机构:
Louis-le-Grand
创建时间:
2022-01-01
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