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

Bainiaksinaite, Julija
Kaplis, Dr Nikolaos
Treleaven, Prof Philip
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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.
Data, Text and Web Mining for Business Analytics, automatic labelling, finance, information retrieval, machine learning, news event detection
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