TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers

Mohammed R. H. Qwaider, Abed Alhakim Freihat, Fausto Giunchiglia


Abstract
In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy.Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis.Our system obtained a comparable resultsto Machine learning systems.
Anthology ID:
S17-2043
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
271–274
Language:
URL:
https://www.aclweb.org/anthology/S17-2043
DOI:
10.18653/v1/S17-2043
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2043.pdf