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
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 339–343
- Language:
- URL:
- https://aclanthology.org/S17-2055
- DOI:
- 10.18653/v1/S17-2055
- Cite (ACL):
- Filip Šaina, Toni Kukurin, Lukrecija Puljić, Mladen Karan, and Jan Šnajder. 2017. TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 339–343, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA (Šaina et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S17-2055.pdf