Ou, S.; Khoo, C.; Goh, D.H.; Heng, H.-Y.: Automatic discourse parsing of sociology dissertation abstracts as sentence categorization (2004)
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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
