A crowdsourcing approach for annotating causal relation instances in Wikipedia

Kazuaki Hanawa, Akira Sasaki, Naoaki Okazaki, Kentaro Inui

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

This paper presents a crowdsourcing approach for annotating causal relation instances to Wikipedia. Because an annotation task cannot be decomposed into multiple-choice problems, we integrate a crowdsourcing service and brat, a popular on-line annotation tool, to provide an easy-to-use interface and quality control for annotation work. We design simple micro-tasks that involve annotating textual spans with causal relations. We issued the micro-tasks to crowd workers, and collected 95,008 annotations of causal relation instances among 8,745 summary sentences in 1,494 Wikipedia articles. The annotated corpus not only provides supervision data for automatic recognition of causal relation instances, but also reveals valuable facts for improving the annotation process of this task.

Original languageEnglish
Pages336-345
Number of pages10
Publication statusPublished - 2019 Jan 1
Event31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017 - Cebu City, Philippines
Duration: 2017 Nov 162017 Nov 18

Conference

Conference31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017
CountryPhilippines
CityCebu City
Period17/11/1617/11/18

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

Fingerprint Dive into the research topics of 'A crowdsourcing approach for annotating causal relation instances in Wikipedia'. Together they form a unique fingerprint.

  • Cite this

    Hanawa, K., Sasaki, A., Okazaki, N., & Inui, K. (2019). A crowdsourcing approach for annotating causal relation instances in Wikipedia. 336-345. Paper presented at 31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017, Cebu City, Philippines.