@inproceedings{nandi-etal-2017-iit,
title = "{IIT}-{UHH} at {S}em{E}val-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification",
author = "Nandi, Titas and
Biemann, Chris and
Yimam, Seid Muhie and
Gupta, Deepak and
Kohail, Sarah and
Ekbal, Asif and
Bhattacharyya, Pushpak",
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/S17-2009",
doi = "10.18653/v1/S17-2009",
pages = "90--97",
abstract = "In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3. We develop a Support Vector Machine (SVM) based system that makes use of textual, domain-specific, word-embedding and topic-modeling features. In addition, we propose a novel method for dialogue chain identification in comment threads. Our primary submission won subtask C, outperforming other systems in all the primary evaluation metrics. We performed well in other English subtasks, ranking third in subtask A and eighth in subtask B. We also developed open source toolkits for all the three English subtasks by the name cQARank [\url{https://github.com/TitasNandi/cQARank}].",
}
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<abstract>In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3. We develop a Support Vector Machine (SVM) based system that makes use of textual, domain-specific, word-embedding and topic-modeling features. In addition, we propose a novel method for dialogue chain identification in comment threads. Our primary submission won subtask C, outperforming other systems in all the primary evaluation metrics. We performed well in other English subtasks, ranking third in subtask A and eighth in subtask B. We also developed open source toolkits for all the three English subtasks by the name cQARank [https://github.com/TitasNandi/cQARank].</abstract>
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%0 Conference Proceedings
%T IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification
%A Nandi, Titas
%A Biemann, Chris
%A Yimam, Seid Muhie
%A Gupta, Deepak
%A Kohail, Sarah
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%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 nandi-etal-2017-iit
%X In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3. We develop a Support Vector Machine (SVM) based system that makes use of textual, domain-specific, word-embedding and topic-modeling features. In addition, we propose a novel method for dialogue chain identification in comment threads. Our primary submission won subtask C, outperforming other systems in all the primary evaluation metrics. We performed well in other English subtasks, ranking third in subtask A and eighth in subtask B. We also developed open source toolkits for all the three English subtasks by the name cQARank [https://github.com/TitasNandi/cQARank].
%R 10.18653/v1/S17-2009
%U https://aclanthology.org/S17-2009
%U https://doi.org/10.18653/v1/S17-2009
%P 90-97
Markdown (Informal)
[IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification](https://aclanthology.org/S17-2009) (Nandi et al., SemEval 2017)
ACL