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Word Embedding for Rhetorical Sentence Categorization on Scientific Articles

Journal of ICT Research and Applications (JICTRA) · Vol. 12 No. 2 (2018) · pp. 168–184

DOI
10.5614/itbj.ict.res.appl.2018.12.2.5
ISSN
2338-5499, 2337-5787
Published
2018-09-28
Publisher
DRI - ITB
Language
eng

Abstract

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%.

Keywords

Collaboration network of the authors

20 authors · 36 co-authorship links · 3 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Institute of Technology
  • Indonesia University of Education
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Bambang Riyanto TrilaksonoBandung Institute of Technology6
E. Aminudin AzizIndonesia University of Education6
Ayu PurwariantiBandung Institute of Technology2
Benhard SitohangBandung Institute of Technology2
Muhammad Haris MaulanaBandung Institute of Technology1
Suhono Harso SupangkatBandung Institute of Technology1
Ahmad DahlanBandung Institute of Technology1
Afrida HelenBandung Institute of Technology1
Jan Wira Gotama PutraBandung Institute of Technology1
Carmadi MachbubBandung Institute of Technology1
Wahyudi WahyudiBandung Institute of Technology1
Ary Setijadi PrihatmantoBandung Institute of Technology1
Fariska Zakhralativa RuskandaBandung Institute of Technology1
Gusti Ayu Putri SaptawatiBandung Institute of Technology1
Nasy`an Taufiq Al GhifariBandung Institute of Technology1
Richard ReinhartBandung Institute of Technology1
Bariza HaqiBandung Institute of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Word Embedding for Rhetorical Sentence Categorization on Scientific Articles</dc:title>
	<dc:creator>Rachman, Ghoziyah Haitan</dc:creator>
	<dc:creator>Khodra, Masayu Leylia</dc:creator>
	<dc:creator>Widyantoro, Dwi Hendratmo</dc:creator>
	<dc:subject xml:lang="en-US">GloVe</dc:subject>
	<dc:subject xml:lang="en-US">rhetorical sentence categorization</dc:subject>
	<dc:subject xml:lang="en-US">scientific article</dc:subject>
	<dc:subject xml:lang="en-US">word embedding</dc:subject>
	<dc:subject xml:lang="en-US">Word2Vec.</dc:subject>
	<dc:description xml:lang="en-US">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%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2018-09-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/6817</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2018.12.2.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 12 No. 2 (2018); 168-184</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2018.12.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/6817/3465</dc:relation>
</oai_dc:dc>