Abstract
We present the approach developed at the Faculty of Engineering of the University of Porto to participate in SemEval-2018 Task 5: Counting Events and Participants within Highly Ambiguous Data covering a very long tail. The work described here presents the experimental system developed to extract entities from news articles for the sake of Question Answering. We propose a supervised learning approach to enable the recognition of two different types of entities: Locations and Participants. We also discuss the use of distance-based algorithms (using Levenshtein distance and Q-grams) for the detection of documents’ closeness based on the entities extracted. For the experiments, we also used a multi-agent system that improved the performance.- Anthology ID:
- S18-1109
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 667–673
- Language:
- URL:
- https://aclanthology.org/S18-1109
- DOI:
- 10.18653/v1/S18-1109
- Cite (ACL):
- Carla Abreu and Eugénio Oliveira. 2018. FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 667–673, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System (Abreu & Oliveira, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/S18-1109.pdf