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
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 344–348
- Language:
- URL:
- https://aclanthology.org/S17-2056
- DOI:
- 10.18653/v1/S17-2056
- Cite (ACL):
- Nada Almarwani and Mona Diab. 2017. GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 344–348, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- GW_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora (Almarwani & Diab, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/S17-2056.pdf