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  • 标题:Word Embedding for Rhetorical Sentence Categorization on Scientific Articles
  • 本地全文:下载
  • 作者:Ghoziyah Haitan Rachman ; Masayu Leylia Khodra ; Dwi Hendratmo Widyantoro
  • 期刊名称:Journal of ICT Research and Applications
  • 印刷版ISSN:2337-5787
  • 电子版ISSN:2338-5499
  • 出版年度:2018
  • 卷号:12
  • 期号:2
  • 页码:168-184
  • 语种:English
  • 出版社:Institut Teknologi Bandung
  • 其他摘要:A common task in summarizing scientific articles is employing the rhetorical structure of sentences. Determining rhetorical sentences itself passes through the process of text categorization. In order to get good performance, some works in text categorization have been done by employing word embedding. This paper presents rhetorical sentence categorization of scientific articles by using word embedding to capture semantically similar words. A comparison of employing Word2Vec and GloVe is shown. First, two experiments are evaluated using five classifiers, namely Naïve Bayes, Linear SVM, IBK, J48, and Maximum Entropy. Then, the best classifier from the first two experiments was employed. This research showed that Word2Vec CBOW performed better than Skip-Gram and GloVe. The best experimental result was from Word2Vec CBOW for 20,155 resource papers from ACL-ARC, features from Teufel and the previous label feature. In this experiment, Linear SVM produced the highest F-measure performance at 43.44%.
  • 关键词:GloVe;rhetorical sentence categorization;scientific article;word embedding;Word2Vec.
  • 其他关键词:GloVe;rhetorical sentence categorization;scientific article;word embedding;Word2Vec.
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