@InProceedings{nandi-EtAl:2017:SemEval,
  author    = {Nandi, Titas  and  Biemann, Chris  and  Yimam, Seid Muhie  and  Gupta, Deepak  and  Kohail, Sarah  and  Ekbal, Asif  and  Bhattacharyya, Pushpak},
  title     = {IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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 [https://github.com/TitasNandi/cQARank].},
  url       = {http://www.aclweb.org/anthology/S17-2009}
}