@inproceedings{xie-etal-2017-eica,
title = "{EICA} Team at {S}em{E}val-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering",
author = "Xie, Yufei and
Wang, Maoquan and
Ma, Jing and
Jiang, Jian and
Lu, Zhao",
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/S17-2047",
doi = "10.18653/v1/S17-2047",
pages = "292--298",
abstract = "We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50{\%}.",
}
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<abstract>We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50%.</abstract>
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%0 Conference Proceedings
%T EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering
%A Xie, Yufei
%A Wang, Maoquan
%A Ma, Jing
%A Jiang, Jian
%A Lu, Zhao
%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 xie-etal-2017-eica
%X We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50%.
%R 10.18653/v1/S17-2047
%U https://aclanthology.org/S17-2047
%U https://doi.org/10.18653/v1/S17-2047
%P 292-298
Markdown (Informal)
[EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering](https://aclanthology.org/S17-2047) (Xie et al., SemEval 2017)
ACL