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ALgA: Automatic logic gate annotator for building financial news events detectors

Bainiaksinaite, J; Kaplis, N; Treleaven, P; (2021) ALgA: Automatic logic gate annotator for building financial news events detectors. In: Proceedings of the 54th Hawaii International Conference on System Sciences. (pp. pp. 1050-1060). Hawaii International Conference on System Sciences Green open access

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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.

Type: Proceedings paper
Title: ALgA: Automatic logic gate annotator for building financial news events detectors
Event: Hawaii International Conference on System Sciences 2021
ISBN-13: 9780998133140
Open access status: An open access version is available from UCL Discovery
Publisher version: http://hdl.handle.net/10125/70740
Language: English
Additional information: This item is licensed under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10130732
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