IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification
TL
TL;DR
A Support Vector Machine (SVM) based system that makes use of textual, domain-specific, word-embedding and topic-modeling features, and a novel method for dialogue chain identification in comment threads is proposed.
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].
References16 items
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SemEval-2017 Task 3: Community Question Answering
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ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering
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Thread-Level Information for Comment Classification in Community Question Answering
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VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems
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