@inproceedings{almarwani-diab-2017-gw,
title = "{GW}{\_}{QA} at {S}em{E}val-2017 Task 3: Question Answer Re-ranking on {A}rabic Fora",
author = "Almarwani, Nada and
Diab, Mona",
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/S17-2056",
doi = "10.18653/v1/S17-2056",
pages = "344--348",
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{\%}.",
}
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<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%.</abstract>
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%0 Conference Proceedings
%T GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora
%A Almarwani, Nada
%A Diab, Mona
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, Canada
%F almarwani-diab-2017-gw
%X 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%.
%R 10.18653/v1/S17-2056
%U https://aclanthology.org/S17-2056
%U https://doi.org/10.18653/v1/S17-2056
%P 344-348
Markdown (Informal)
[GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora](https://aclanthology.org/S17-2056) (Almarwani & Diab, SemEval 2017)
ACL