Abstract Sentence Classification for Scientific Papers Based on Transductive SVM

Yuanchao Liu, Feng Wu, Ming Liu, Bingquan Liu

Abstract


Presently, sentence-level researches are very significant in fields like natural language processing, information retrieval, machine translation etc. In this paper we present a practical task on sentence classification. The main purpose of this work is to classify the abstract sentences of scientific papers in the corpus built by ourselves into four categories- the background, the goal, the method and the result- which differ from each other in common usage, so that we can do further researches such as frequent pattern mining, information extraction and making a corpus for writing assistant system of scientific paper with these results. The main method of the classification is the Support Vector Machine, which is acknowledged among the best machine learning methods in the common text classification tasks. A semi-supervised method, Transductive Support Vector Machine, is also introduced into this four-class classification task to improve the accuracy. The experiments are conducted upon the corpus made by ourselves that consists of abstract sentences of scientific papers. The accuracy of the classifier finally reaches 75.86% with the semi-supervised method.


Full Text: PDF DOI: 10.5539/cis.v6n4p125

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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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