@inproceedings{qi-etal-2017-scir,
title = "{SCIR}-{QA} at {S}em{E}val-2017 Task 3: {CNN} Model Based on Similar and Dissimilar Information between Keywords for Question Similarity",
author = "Qi, Le and
Zhang, Yu and
Liu, Ting",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
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/S17-2049",
doi = "10.18653/v1/S17-2049",
pages = "305--309",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity
%A Qi, Le
%A Zhang, Yu
%A Liu, Ting
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qi-etal-2017-scir
%X 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.
%R 10.18653/v1/S17-2049
%U https://aclanthology.org/S17-2049
%U https://doi.org/10.18653/v1/S17-2049
%P 305-309
Markdown (Informal)
[SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity](https://aclanthology.org/S17-2049) (Qi et al., SemEval 2017)
ACL