Abstract
In this paper we describe our QU-BIGIR system for the Arabic subtask D of the SemEval 2017 Task 3. Our approach builds on our participation in the past version of the same subtask. This year, our system uses different similarity measures that encodes lexical and semantic pairwise similarity of text pairs. In addition to well known similarity measures such as cosine similarity, we use other measures based on the summary statistics of word embedding representation for a given text. To rank a list of candidate question answer pairs for a given question, we learn a linear SVM classifier over our similarity features. Our best resulting run came second in subtask D with a very competitive performance to the first-ranking system.- Anthology ID:
- S17-2059
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 360–364
- Language:
- URL:
- https://aclanthology.org/S17-2059
- DOI:
- 10.18653/v1/S17-2059
- Cite (ACL):
- Marwan Torki, Maram Hasanain, and Tamer Elsayed. 2017. QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 360–364, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums (Torki et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2059.pdf