Enhanced Integrated Scoring for Cleaning Dirty Texts

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dc.creatorWong, Wilson
dc.creatorLiu, Wei
dc.creatorBennamoun, Mohammed
dc.date2008-10-02
dc.date.accessioned2026-07-07T10:07:01Z
dc.date.available2026-07-07T10:07:01Z
dc.descriptionAn increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evaluations performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98% as compared to 96.5% and 71% based on the use of only basic ISSAC and of Aspell, respectively.
dc.descriptionMore information is available at http://explorer.csse.uwa.edu.au/reference/
dc.identifierhttps://arxiv.org/abs/0810.0332
dc.identifierhttp://arxiv.org/abs/0810.0332
dc.identifierIJCAI Workshop on Analytics for Noisy Unstructured Text Data (AND), 2007, pages 55-62
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170489
dc.subjectArtificial Intelligence
dc.titleEnhanced Integrated Scoring for Cleaning Dirty Texts
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