Abstract
Language | English |
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Title of host publication | Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
Publisher | Association for Computational Linguistics |
Pages | 155-164 |
Number of pages | 10 |
Publication status | Published - 2014 |
Event | 52nd Annul Meeting of the Association of Computational Linguistics 2014 - Maryland, Baltimore, United States Duration: 22 Jun 2014 → 27 Jun 2014 |
Conference
Conference | 52nd Annul Meeting of the Association of Computational Linguistics 2014 |
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Country | United States |
City | Baltimore |
Period | 22/06/2014 → 27/06/2014 |
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Unsupervised Solution Post Identification from Discussion Forums. / Padmanabhan, Deepak; Visweswariah, Karthik.
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2014. p. 155-164.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Unsupervised Solution Post Identification from Discussion Forums
AU - Padmanabhan, Deepak
AU - Visweswariah, Karthik
PY - 2014
Y1 - 2014
N2 - Discussion forums have evolved into a dependablesource of knowledge to solvecommon problems. However, only a minorityof the posts in discussion forumsare solution posts. Identifying solutionposts from discussion forums, hence, is animportant research problem. In this paper,we present a technique for unsupervisedsolution post identification leveraginga so far unexplored textual feature, thatof lexical correlations between problemsand solutions. We use translation modelsand language models to exploit lexicalcorrelations and solution post characterrespectively. Our technique is designedto not rely much on structural featuressuch as post metadata since suchfeatures are often not uniformly availableacross forums. Our clustering-based iterativesolution identification approach basedon the EM-formulation performs favorablyin an empirical evaluation, beatingthe only unsupervised solution identificationtechnique from literature by a verylarge margin. We also show that our unsupervisedtechnique is competitive againstmethods that require supervision, outperformingone such technique comfortably.
AB - Discussion forums have evolved into a dependablesource of knowledge to solvecommon problems. However, only a minorityof the posts in discussion forumsare solution posts. Identifying solutionposts from discussion forums, hence, is animportant research problem. In this paper,we present a technique for unsupervisedsolution post identification leveraginga so far unexplored textual feature, thatof lexical correlations between problemsand solutions. We use translation modelsand language models to exploit lexicalcorrelations and solution post characterrespectively. Our technique is designedto not rely much on structural featuressuch as post metadata since suchfeatures are often not uniformly availableacross forums. Our clustering-based iterativesolution identification approach basedon the EM-formulation performs favorablyin an empirical evaluation, beatingthe only unsupervised solution identificationtechnique from literature by a verylarge margin. We also show that our unsupervisedtechnique is competitive againstmethods that require supervision, outperformingone such technique comfortably.
M3 - Conference contribution
SP - 155
EP - 164
BT - Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
PB - Association for Computational Linguistics
ER -