Mihaylov, Todor ; Balchev, Daniel ; Kiprov, Yasen ; Koychev, Ivan ; Nakov, Preslav (2017)
Large-Scale Goodness Polarity Lexicons for Community Question Answering.
Shinjuku, Tokyo, Japan
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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>
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Mihaylov, Todor ; Balchev, Daniel ; Kiprov, Yasen ; Koychev, Ivan ; Nakov, Preslav |
| Art des Eintrags: | Bibliographie |
| Titel: | Large-Scale Goodness Polarity Lexicons for Community Question Answering |
| Sprache: | Englisch |
| Publikationsjahr: | August 2017 |
| Verlag: | ACM |
| Buchtitel: | SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Veranstaltungsort: | Shinjuku, Tokyo, Japan |
| URL / URN: | http://delivery.acm.org/10.1145/3090000/3080757/p1185-mihayl... |
| Zugehörige Links: | |
| Kurzbeschreibung (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> |
| Freie Schlagworte: | Community Question Answering;AIPHES_area_a2 |
| ID-Nummer: | TUD-CS-2017-0298 |
| Fachbereich(e)/-gebiet(e): | DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 24 Nov 2017 15:45 |
| Letzte Änderung: | 28 Sep 2018 15:15 |
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