Automatic acquisition of lexical knowledge from sparse and noisy data
Identifieur interne : 002252 ( Main/Exploration ); précédent : 002251; suivant : 002253Automatic acquisition of lexical knowledge from sparse and noisy data
Auteurs : René Schneider [Allemagne, Colombie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1998.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Abstract: Optical character recognition (OCR) still garbles a considerable amount of information reduction and noise on texts so that many documents are unsuitable for information extraction systems. This paper introduces a statistical method for bootstrapping a lexicon from a very small number of “noisy ,” domain-specific texts. This method determines regularity in grammatical forms and also reoccuring ungrammatical forms from the input text. Through a combination of frequency lists and Levenshtein matrices, a language independent, robust core lexicon is constructed that supports the analysis of “noisy texts,” too.
Url:
DOI: 10.1007/BFb0026670
Affiliations:
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- to stream PascalFrancis, to step Corpus: 000888
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Le document en format XML
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