GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora

Nada Almarwani, Mona Diab


Abstract
This paper describes our submission to SemEval-2017 Task 3 Subtask D, “Question Answer Ranking in Arabic Community Question Answering”. In this work, we applied a supervised machine learning approach to automatically re-rank a set of QA pairs according to their relevance to a given question. We employ features based on latent semantic models, namely WTMF, as well as a set of lexical features based on string lengths and surface level matching. The proposed system ranked first out of 3 submissions, with a MAP score of 61.16%.
Anthology ID:
S17-2056
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:
344–348
Language:
URL:
https://www.aclweb.org/anthology/S17-2056
DOI:
10.18653/v1/S17-2056
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2056.pdf