An Assessment of Substitute Words in the Context of Academic Writing Proposed by Pre-trained and Specific Word Embedding Models

Chooi Ling Goh, Yves Lepage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Researchers who are non-native speakers of English always face some problems when composing scientific articles in this language. Most of the time, it is due to lack of vocabulary or knowledge of alternate ways of expression. In this paper, we suggest to use word embeddings to look for substitute words used for academic writing in a specific domain. Word embeddings may not only contain semantically similar words but also other words with similar word vectors, that could be better expressions. A word embedding model trained on a collection of academic articles in a specific domain might suggest similar expressions that comply to that writing style and are suited to that domain. Our experiment results show that a word embedding model trained on the NLP domain is able to propose possible substitutes that could be used to replace the target words in a certain context.

Original languageEnglish
Title of host publicationComputational Linguistics - 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Revised Selected Papers
EditorsLe-Minh Nguyen, Satoshi Tojo, Xuan-Hieu Phan, Kôiti Hasida
PublisherSpringer
Pages414-427
Number of pages14
ISBN (Print)9789811561672
DOIs
Publication statusPublished - 2020
Event16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019 - Hanoi, Viet Nam
Duration: 2019 Oct 112019 Oct 13

Publication series

NameCommunications in Computer and Information Science
Volume1215 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019
CountryViet Nam
CityHanoi
Period19/10/1119/10/13

Keywords

  • Academic writing
  • Dictionary lookup
  • Synonym
  • Word embedding
  • Word similarity

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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