TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA

Filip Šaina, Toni Kukurin, Lukrecija Puljić, Mladen Karan, Jan Šnajder


Abstract
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, including two supervised learning models – Support Vector Machines (SVM) and Convolutional 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 embeddings. 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 organizers. 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.
Anthology ID:
S17-2055
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
339–343
Language:
URL:
https://www.aclweb.org/anthology/S17-2055
DOI:
10.18653/v1/S17-2055
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2055.pdf