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Publication Detail
ALgA: Automatic logic gate annotator for building financial news events detectors
  • Publication Type:
    Conference
  • Authors:
    Bainiaksinaite J, Kaplis N, Treleaven P
  • Publication date:
    05/01/2021
  • Pagination:
    1050, 1060
  • Published proceedings:
    Proceedings of the Annual Hawaii International Conference on System Sciences
  • Volume:
    2020-January
  • ISBN-13:
    9780998133140
  • Status:
    Published
  • Name of conference:
    HiCSS 2021
  • Print ISSN:
    1530-1605
Abstract
We present a new automatic data labelling framework called ALGA - Automatic Logic Gate Annotator. The framework helps to create large amounts of annotated data for training domain-specific financial news events detection classifiers quicker. ALGA framework implements a rules-based approach to annotate a training dataset. This method has following advantages: 1) unlike traditional data labelling methods, it helps to filter relevant news articles from noise; 2) allows easier transferability to other domains and better interpretability of models trained on automatically labelled data. To create this framework, we focus on the U.S.-based companies that operate in the Apparel and Footwear industry. We show that event detection classifiers trained on the data generated by our framework can achieve state-of-the-art performance in the domain-specific financial events detection task. Besides, we create a domain-specific events synonyms dictionary.
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