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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.

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