| Summary: | 碩士 === 輔仁大學 === 資訊工程學系碩士班 === 103 === English of Academic Writing (EAW) is essential to the research community for sharing knowledge. Research documents using EAW, especially the abstract and introduction, may follow a simple and succinct picture of the organizational patterns, called ‘move’. This paper introduces a method for computational analysis of move structures, the Background-Purpose-Method-Result-Conclusion in this paper, in abstracts and introductions of research documents, instead of manually time-consuming and labor-intensive analysis process. In our approach, sentences in a given abstract and introduction are automatically analyzed and labeled with a specific move (i.e., B-P-M-R-C in this paper) to reveal various rhetorical functions. As a result, it is expected that the automatic analytical tool for move structures will facilitate non-native speakers or novice writers to be aware of appropriate move structures and internalize relevant knowledge to improve their writing.
In this paper, we propose a Bayesian approach to determine move tags for research articles. The approach consists of two phases, training phase and testing phase. In the training phase, we build a Bayesian model based on a couples of given ‘initial patterns’ and the corpus, a subset of CiteSeerX. In the beginning, the priori probability of Bayesian model solely relies on ‘initial patterns’. Subsequently, with respect to the corpus, we process each document one by one: extract features, determine tags, and update the Bayesian model iteratively. In the testing phase, we compare our results with tags which are manually assigned by the experts. In our experiments, the promising accuracy of the proposed approach reaches 56%.
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