engleski

TakeLab-QA at SemEval-2017 task 3: Classification experiments for answer retrieval in community QA

In this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is a question-comment re-ranking problem. We present a classification based approach, in- cluding two supervised learning models – Support Vector Machines (SVM) and Con- volutional Neural Networks (CNN). We use features based on different semantic similarity models (e.g., Latent Dirichlet Allocation), as well as features based on several types of pre-trained word embed- dings. Moreover, we also use some hand- crafted task-specific features. For training, our system uses no external labeled data apart from that provided by the organiz- ers. Our primary submission achieves a MAP-score of 81.14 and F1-score of 66.99 – ranking us 10th on the SemEval 2017 task 3, subtask A.

Community QA, Learning to rank

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