@inproceedings{qwaider-etal-2017-trentoteam,
title = "{T}rento{T}eam at {S}em{E}val-2017 Task 3: An application of {G}rice Maxims in Ranking Community Question Answers",
author = "Qwaider, Mohammed R. H. and
Freihat, Abed Alhakim and
Giunchiglia, Fausto",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2043",
doi = "10.18653/v1/S17-2043",
pages = "271--274",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
%A Qwaider, Mohammed R. H.
%A Freihat, Abed Alhakim
%A Giunchiglia, Fausto
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qwaider-etal-2017-trentoteam
%X 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.
%R 10.18653/v1/S17-2043
%U https://aclanthology.org/S17-2043
%U https://doi.org/10.18653/v1/S17-2043
%P 271-274
Markdown (Informal)
[TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers](https://aclanthology.org/S17-2043) (Qwaider et al., SemEval 2017)
ACL