Questioning the question - Addressing the answerability of questions in community question-answering

Chirag Shah, Vanessa Kitzie, Erik Choi

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

16 Scopus citations

Abstract

In this paper, we investigate question quality among questions posted in Yahoo! Answers to assess what factors contribute to the goodness of a question and determine if we can flag poor quality questions. Using human assessments of whether a question is good or bad and extracted textual features from the questions, we built an SVM classifier that performed with relatively good classification accuracy for both good and bad questions. We then enhanced the performance of this classifier by using additional human assessments of question type as well as additional question features to first separate questions by type and then classify them. This two-step classifier improved the performance of the original classifier in identifying Type II errors and suggests that our model presents a novel approach for identifying bad questions with implications for query revision and routing.

Original languageEnglish (US)
Title of host publicationProceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014
PublisherIEEE Computer Society
Pages1386-1395
Number of pages10
ISBN (Print)9781479925049
DOIs
StatePublished - 2014
Event47th Hawaii International Conference on System Sciences, HICSS 2014 - Waikoloa, HI, United States
Duration: Jan 6 2014Jan 9 2014

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Other

Other47th Hawaii International Conference on System Sciences, HICSS 2014
Country/TerritoryUnited States
CityWaikoloa, HI
Period1/6/141/9/14

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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