Abstract
We describe a method of calculating the similarity of questions in community QA. Question in cQA are usually very long and there are a lot of useless information about calculating the similarity of questions. Therefore,we implement a CNN model based on similar and dissimilar information between question’s keywords. We extract the keywords of questions, and then model the similar and dissimilar information between the keywords, and use the CNN model to calculate the similarity.- Anthology ID:
- S17-2049
- 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:
- 305–309
- Language:
- URL:
- https://aclanthology.org/S17-2049
- DOI:
- 10.18653/v1/S17-2049
- Cite (ACL):
- Le Qi, Yu Zhang, and Ting Liu. 2017. SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 305–309, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity (Qi et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2049.pdf