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  • × author_ss:"Khoo, C."
  1. Khoo, C.; Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction (2002) 0.01 
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    Abstract
    Automatic identification of semantic relations in text is a difficult problem, but is important for many applications. It has been used for relation matching in information retrieval to retrieve documents that contain not only the concepts but also the relations between concepts specified in the user's query. It is an integral part of information extraction-extracting from natural language text, facts or pieces of information related to a particular event or topic. Other potential applications are in the construction of relational thesauri (semantic networks of related concepts) and other kinds of knowledge bases, and in natural language processing applications such as machine translation and computer comprehension of text. This chapter examines the main methods used for identifying semantic relations automatically and their application in information retrieval and information extraction.
    Series
    Information science and knowledge management; vol.3
  2. Poo, D.C.C.; Khoo, C.: Subject searching in online catalog systems (1997) 0.00 
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    Source
    Encyclopedia of library and information science. Vol.60, [=Suppl.23]
  3. Wang, Z.; Chaudhry, A.S.; Khoo, C.: Support from bibliographic tools to build an organizational taxonomy for navigation : use of a general classification scheme and domain thesauri (2010) 0.00 
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    Abstract
    A study was conducted to investigate the capability of a general classification scheme and domain thesauri to support the construction of an organizational taxonomy to be used for navigation, and to develop steps and guidelines for constructing the hierarchical structure and categories. The study was conducted in the context of a graduate department in information studies in Singapore that offers Master's and PhD programs in information studies, information systems, and knowledge management. An organizational taxonomy, called Information Studies Taxonomy, was built for learning, teaching and research tasks of the department using the Dewey Decimal Classification and three domain thesauri (ASIS&T, LISA, and ERIC). The support and difficulties of using the general classification scheme and domain thesauri were identified in the taxonomy development process. Steps and guidelines for constructing the hierarchical structure and categories were developed based on problems encountered in using the sources.
  4. Abdul, H.; Khoo, C.: Automatic indexing of medical literature using phrase matching : an exploratory study 0.00 
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    Source
    Health information: new directions. Proceedings of the Joint Conference of the Health Libraries Sections of the Australian Library and Information Association and New Zealand Library Association, Auckland, New Zealand, 12.-16.11.1989
  5. Zhonghong, W.; Chaudhry, A.S.; Khoo, C.: Potential and prospects of taxonomies for content organization (2006) 0.00 
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    Abstract
    While taxonomies are being increasingly discussed in published and grey literature, the term taxonomy still seems to be stated quite loosely and obscurely. This paper aims at explaining and clarifying the concept of taxonomy in the context of information organization. To this end, the salient features of taxonomies are identified and their scope, nature, and role are further elaborated based on an extensive literature review. In the meantime, the connection and distinctions between taxonomies and classification schemes and thesauri are also identified, and the rationale that taxonomies are chosen as a viable knowledge organization system used in organization-wide websites to support browsing and aid navigation is clarified.
  6. Ou, S.; Khoo, C.; Goh, D.H.; Heng, H.-Y.: Automatic discourse parsing of sociology dissertation abstracts as sentence categorization (2004) 0.00 
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    Abstract
    We investigated an approach to automatic discourse parsing of sociology dissertation abstracts as a sentence categorization task. Decision tree induction was used for the automatic categorization. Three models were developed. Model 1 made use of word tokens found in the sentences. Model 2 made use of both word tokens and sentence position in the abstract. In addition to the attributes used in Model 2, Model 3 also considered information regarding the presence of indicator words in surrounding sentences. Model 3 obtained the highest accuracy rate of 74.5 % when applied to a test sample, compared to 71.6% for Model 2 and 60.8% for Model 1. The results indicated that information about sentence position can substantially increase the accuracy of categorization, and indicator words in earlier sentences (before the sentence being processed) also contribute to the categorization accuracy.
    Source
    Knowledge organization and the global information society: Proceedings of the 8th International ISKO Conference 13-16 July 2004, London, UK. Ed.: I.C. McIlwaine
  7. Khoo, C.; Chan, S.; Niu, Y.: ¬The many facets of the cause-effect relation (2002) 0.00 
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    Series
    Information science and knowledge management; vol.3
  8. Na, J.-C.; Sui, H.; Khoo, C.; Chan, S.; Zhou, Y.: Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews (2004) 0.00 
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    Source
    Knowledge organization and the global information society: Proceedings of the 8th International ISKO Conference 13-16 July 2004, London, UK. Ed.: I.C. McIlwaine
  9. Lee, C.-H.; Khoo, C.; Na, J.-C.: Automatic identification of treatment relations for medical ontology learning : an exploratory study (2004) 0.00 
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    Source
    Knowledge organization and the global information society: Proceedings of the 8th International ISKO Conference 13-16 July 2004, London, UK. Ed.: I.C. McIlwaine

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