Abstract
To understand and automatically extract information about events presented in a text, semantically meaningful units expressing these events are important. Extracting events and classifying them into event types and subtypes using Natural Language Processing techniques poses a challenging research problem. There is no clear-cut definitions to what an event from a text is and what the optimal representation of semantic units within a given text is. In addition, events in a text can be classified into types and subtypes of events; and a single event can have multiple mentions in a given sentence. In this paper, we propose a model to determine events within a given text and classify them into event types or subtypes and REALIS by the distributional semantic role labeling and neural embedding techniques. For the task of the event nugget detection, we trained a three-layer network to determine the event mentions from texts achieving F1-score of 77.37 % for macro average and 71.10 % for micro average, respectively.
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Ceesay, B., Hou, WJ. (2016). Event Extraction and Classification by Neural Network Model. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_20
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DOI: https://doi.org/10.1007/978-3-319-42007-3_20
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