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Large-Scale Goodness Polarity Lexicons for Community Question Answering

Mihaylov, Todor and Balchev, Daniel and Kiprov, Yasen and Koychev, Ivan and Nakov, Preslav (2017):
Large-Scale Goodness Polarity Lexicons for Community Question Answering.
In: SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Shinjuku, Tokyo, Japan, [Online-Edition: http://delivery.acm.org/10.1145/3090000/3080757/p1185-mihayl...],
[Conference or Workshop Item]

Abstract

<span style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12.8px; ">We transfer a key idea from the field of sentiment analysis to a new domain: community question answering (cQA). The cQA task we are interested in is the following: given a question and a thread of comments, we want to re-rank the comments, so that the ones that are good answers to the question would be ranked higher than the bad ones. We notice that good vs. bad comments use specific vocabulary and that one can often predict the goodness/badness of a comment even ignoring the question, based on the comment contents only. This leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis. In particular, we use pointwise mutual information in order to build large-scale goodness polarity lexicons in a semi-supervised manner starting with a small number of initial seeds. The evaluation results show an improvement of 0.7 MAP points absolute over a very strong baseline, and state-of-the art performance on SemEval-2016 Task 3.</span>

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Mihaylov, Todor and Balchev, Daniel and Kiprov, Yasen and Koychev, Ivan and Nakov, Preslav
Title: Large-Scale Goodness Polarity Lexicons for Community Question Answering
Language: English
Abstract:

<span style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12.8px; ">We transfer a key idea from the field of sentiment analysis to a new domain: community question answering (cQA). The cQA task we are interested in is the following: given a question and a thread of comments, we want to re-rank the comments, so that the ones that are good answers to the question would be ranked higher than the bad ones. We notice that good vs. bad comments use specific vocabulary and that one can often predict the goodness/badness of a comment even ignoring the question, based on the comment contents only. This leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis. In particular, we use pointwise mutual information in order to build large-scale goodness polarity lexicons in a semi-supervised manner starting with a small number of initial seeds. The evaluation results show an improvement of 0.7 MAP points absolute over a very strong baseline, and state-of-the art performance on SemEval-2016 Task 3.</span>

Title of Book: SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher: ACM
Uncontrolled Keywords: Community Question Answering;AIPHES_area_a2
Divisions: DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Shinjuku, Tokyo, Japan
Date Deposited: 24 Nov 2017 15:45
Official URL: http://delivery.acm.org/10.1145/3090000/3080757/p1185-mihayl...
Identification Number: TUD-CS-2017-0298
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