Abstract
This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.- Anthology ID:
- S17-2062
- 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:
- 375–379
- Language:
- URL:
- https://aclanthology.org/S17-2062
- DOI:
- 10.18653/v1/S17-2062
- Cite (ACL):
- Byron Galbraith, Bhanu Pratap, and Daniel Shank. 2017. Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 375–379, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection (Galbraith et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/S17-2062.pdf