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://aclanthology.org/S17-2043
- DOI:
- 10.18653/v1/S17-2043
- Cite (ACL):
- Mohammed R. H. Qwaider, Abed Alhakim Freihat, and Fausto Giunchiglia. 2017. TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 271–274, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers (Qwaider et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S17-2043.pdf