ALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors

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dc.contributor.authorBainiaksinaite, Julija
dc.contributor.authorKaplis, Dr Nikolaos
dc.contributor.authorTreleaven, Prof Philip
dc.date.accessioned2020-12-24T19:11:33Z
dc.date.available2020-12-24T19:11:33Z
dc.date.issued2021-01-05
dc.description.abstractWe 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.
dc.format.extent11 pages
dc.identifier.doi10.24251/HICSS.2021.128
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70740
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData, Text and Web Mining for Business Analytics
dc.subjectautomatic labelling
dc.subjectfinance
dc.subjectinformation retrieval
dc.subjectmachine learning
dc.subjectnews event detection
dc.titleALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors
prism.startingpage1050

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