@inproceedings{abreu-oliveira-2018-feup,
    title = "{FEUP} at {S}em{E}val-2018 Task 5: An Experimental Study of a Question Answering System",
    author = "Abreu, Carla  and
      Oliveira, Eug{\'e}nio",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S18-1109/",
    doi = "10.18653/v1/S18-1109",
    pages = "667--673",
    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."
}