FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System

Carla Abreu, Eugénio Oliveira


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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
667–673
Language:
URL:
https://www.aclweb.org/anthology/S18-1109
DOI:
10.18653/v1/S18-1109
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1109.pdf