Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/73289
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dc.titleCorpus building for corporate knowledge discovery and management: A case study of manufacturing
dc.contributor.authorLiu, Y.
dc.contributor.authorLoh, H.T.
dc.date.accessioned2014-06-19T05:33:20Z
dc.date.available2014-06-19T05:33:20Z
dc.date.issued2007
dc.identifier.citationLiu, Y.,Loh, H.T. (2007). Corpus building for corporate knowledge discovery and management: A case study of manufacturing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4692 LNAI (PART 1) : 542-550. ScholarBank@NUS Repository.
dc.identifier.isbn9783540748175
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/73289
dc.description.abstractBuilding a collection of electronic documents, e.g. corpus, is a cornerstone for the research in information retrieval, text mining and knowledge management. In literature, very few papers have discussed the necessary concerns for building a corpus and explained the building process systematically. In this paper, we explain our work of building an enterprise corpus called manufacturing corpus version 1 (MCV1) for corporate knowledge management purpose. Relevant issues, e.g. input texts, category labels and policies, as well as its parallel coding process and quality measurements are discussed. The realworld automated text classification experiments based on MCV1 show the soundness of its coding process. Finally, suggestions are made on how the proposed approach can be implemented in a more economical manner. © Springer-Verlag Berlin Heidelberg 2007.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume4692 LNAI
dc.description.issuePART 1
dc.description.page542-550
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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