Abstract
In this paper, we describe our system for SemEval-2015 Task 3: Answer Selection in Community Question Answering. In this task, the systems are required to identify the good or potentially good answers from the answer thread in Community Question Answering collections. Our system combines 16 features belong to 5 groups to predict answer quality. Our final model achieves the best result in subtask A for English, both in accuracy and F1-score.
Cite
CITATION STYLE
Tran, Q. H., Tran, V. D., Vu, T. T., Le Nguyen, M., & Pham, S. B. (2015). JAIST: Combining multiple features for Answer Selection in Community Question Answering. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 215–219). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2038
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