Identifying informational vs. conversational qestions on community qestion answering archives

Ido Guy, Victor Makarenkov, Niva Hazon, Lior Rokach, Bracha Shapira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Questions on community question answering websites usually reflect one of two intents: learning information or starting a conversation. In this paper, we revisit this fundamental classification task of informational versus conversational questions, which was originally introduced and studied in 2009. We use a substantially larger dataset of archived questions from Yahoo Answers, which includes the question's title, description, answers, and votes. We replicate the original experiments over this dataset, point out the common and different from the original results, and present a broad set of characteristics that distinguish the two question types. We also develop new classifiers that make use of additional data types, advanced machine learning, and a large dataset of unlabeled data, which achieve enhanced performance.

Original languageEnglish
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages216-224
Number of pages9
ISBN (Electronic)9781450355810
DOIs
StatePublished - 2 Feb 2018
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: 5 Feb 20189 Feb 2018

Publication series

NameWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
Volume2018-Febuary

Conference

Conference11th ACM International Conference on Web Search and Data Mining, WSDM 2018
Country/TerritoryUnited States
CityMarina Del Rey
Period5/02/189/02/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

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