@inproceedings{strobl-etal-2020-wexea,
    title = "{WEXEA}: {W}ikipedia {EX}haustive Entity Annotation",
    author = "Strobl, Michael  and
      Trabelsi, Amine  and
      Zaiane, Osmar",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.lrec-1.240/",
    pages = "1951--1958",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "Building predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated text. Wikipedia is often used as a corpus for these tasks where the annotation is a named entity linked by a hyperlink to its article. However, editors on Wikipedia are only expected to link these mentions in order to help the reader to understand the content, but are discouraged from adding links that do not add any benefit for understanding an article. Therefore, many mentions of popular entities (such as countries or popular events in history), or previously linked articles, as well as the article{'}s entity itself, are not linked. In this paper, we discuss WEXEA, a Wikipedia EXhaustive Entity Annotation system, to create a text corpus based on Wikipedia with exhaustive annotations of entity mentions, i.e. linking all mentions of entities to their corresponding articles. This results in a huge potential for additional annotations that can be used for downstream NLP tasks, such as Relation Extraction. We show that our annotations are useful for creating distantly supervised datasets for this task. Furthermore, we publish all code necessary to derive a corpus from a raw Wikipedia dump, so that it can be reproduced by everyone."
}