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  • × author_ss:"Ou, S."
  1. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.00 
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    Abstract
    This study seeks to find out how human beings cluster Web pages naturally. Twenty Web pages retrieved by the Northem Light search engine for each of 10 queries were sorted by 3 subjects into categories that were natural or meaningful to them. lt was found that different subjects clustered the same set of Web pages quite differently and created different categories. The average inter-subject similarity of the clusters created was a low 0.27. Subjects created an average of 5.4 clusters for each sorting. The categories constructed can be divided into 10 types. About 1/3 of the categories created were topical. Another 20% of the categories relate to the degree of relevance or usefulness. The rest of the categories were subject-independent categories such as format, purpose, authoritativeness and direction to other sources. The authors plan to develop automatic methods for categorizing Web pages using the common categories created by the subjects. lt is hoped that the techniques developed can be used by Web search engines to automatically organize Web pages retrieved into categories that are natural to users. 1. Introduction The World Wide Web is an increasingly important source of information for people globally because of its ease of access, the ease of publishing, its ability to transcend geographic and national boundaries, its flexibility and heterogeneity and its dynamic nature. However, Web users also find it increasingly difficult to locate relevant and useful information in this vast information storehouse. Web search engines, despite their scope and power, appear to be quite ineffective. They retrieve too many pages, and though they attempt to rank retrieved pages in order of probable relevance, often the relevant documents do not appear in the top-ranked 10 or 20 documents displayed. Several studies have found that users do not know how to use the advanced features of Web search engines, and do not know how to formulate and re-formulate queries. Users also typically exert minimal effort in performing, evaluating and refining their searches, and are unwilling to scan more than 10 or 20 items retrieved (Jansen, Spink, Bateman & Saracevic, 1998). This suggests that the conventional ranked-list display of search results does not satisfy user requirements, and that better ways of presenting and summarizing search results have to be developed. One promising approach is to group retrieved pages into clusters or categories to allow users to navigate immediately to the "promising" clusters where the most useful Web pages are likely to be located. This approach has been adopted by a number of search engines (notably Northem Light) and search agents.
    Date
    12. 9.2004 9:56:22
    Source
    Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas
  2. 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.
    Content
    1. Introduction This paper reports our initial effort to develop an automatic method for parsing the discourse structure of sociology dissertation abstracts. This study is part of a broader study to develop a method for multi-document summarization. Accurate discourse parsing will make it easier to perform automatic multi-document summarization of dissertation abstracts. In a previous study, we determined that the macro-level structure of dissertation abstracts typically has five sections (Khoo et al., 2002). In this study, we treated discourse parsing as a text categorization problem - assigning each sentence in a dissertation abstract to one of the five predefined sections or categories. Decision tree induction, a machine-learning method, was applied to word tokens found in the abstracts to construct a decision tree model for the categorization purpose. Decision tree induction was selected primarily because decision tree models are easy to interpret and can be converted to rules that can be incorporated in other computer programs. A well-known decision-tree induction program, C5.0 (Quinlan, 1993), was used in this study.
    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
  3. Khoo, C.S.G.; Ou, S.: Machine versus human clustering of concepts across documents (2008) 0.00 
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    Source
    Culture and identity in knowledge organization: Proceedings of the Tenth International ISKO Conference 5-8 August 2008, Montreal, Canada. Ed. by Clément Arsenault and Joseph T. Tennis

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