This paper describes the KeLP system participating in the SemEval-2016 Community Question Answering (cQA) task. The challenge tasks are modeled as binary classification problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and produce the final ranking. All classifiers and kernels have been implemented within the Kernel-based Learning Platform called KeLP. Our primary submission ranked first in Subtask A, third in Subtask B and second in Subtask C. These ranks are based on MAP, which is the referring challenge system score. Our approach outperforms all the other systems with respect to all the other challenge metrics.
Filice, S., Croce, D., Moschitti, A., Basili, R. (2016). KeLP at SemEval-2016 task 3: Learning semantic relations between questions and answers. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (pp.1116-1123). Association for Computational Linguistics (ACL) [10.18653/v1/s16-1172].
KeLP at SemEval-2016 task 3: Learning semantic relations between questions and answers
Filice S.;Croce D.;Moschitti A.;Basili R.
2016-01-01
Abstract
This paper describes the KeLP system participating in the SemEval-2016 Community Question Answering (cQA) task. The challenge tasks are modeled as binary classification problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and produce the final ranking. All classifiers and kernels have been implemented within the Kernel-based Learning Platform called KeLP. Our primary submission ranked first in Subtask A, third in Subtask B and second in Subtask C. These ranks are based on MAP, which is the referring challenge system score. Our approach outperforms all the other systems with respect to all the other challenge metrics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
