SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity

Le Qi, Yu Zhang, Ting Liu


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S17-2049.pdf