Typicality of Lexical Bundles in Different Sections of Scientific Articles

Haotong Wang, Yves Lepage, Chooi Ling Goh

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

Abstract

This paper proposes a method to quantify the typicality of lexical bundles in sections of academic articles, specifically in the field of Natural Language Processing papers. Typicality is defined as the product of individual KL-divergence scores and the probability of a bundle to appear in a type of section. An evaluation of our typicality measure against two other baselines shows slight improvements according to the Silhouette coefficient.

Original languageEnglish
Title of host publicationSSPS 2020 - 2020 2nd Symposium on Signal Processing Systems
PublisherAssociation for Computing Machinery
Pages56-60
Number of pages5
ISBN (Electronic)9781450388627
DOIs
Publication statusPublished - 2020 Jul 11
Event2nd Symposium on Signal Processing Systems, SSPS 2020 - Virtual, Online, China
Duration: 2020 Jul 112020 Jul 13

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd Symposium on Signal Processing Systems, SSPS 2020
CountryChina
CityVirtual, Online
Period20/7/1120/7/13

Keywords

  • lexical bundles
  • typicality
  • writing aid

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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