Event detection without trigger words on movie scripts

Weichuan Xu; Weiwen Zhang; Depei Wang

doi:10.1117/12.2577626

10 November 2020 Event detection without trigger words on movie scripts

Weichuan Xu, Weiwen Zhang, Depei Wang

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841G (2020) https://doi.org/10.1117/12.2577626
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Event extraction is a challenging task in text mining. Current research on event extraction is often based on the sentence level, while the research based on the document level is less investigated. In this paper, we propose a deep learning model, termed BLMA, which combines Bi-LSTM and Multi-head weighted Attention to detect multiple event types. Unlike traditional methods, BLMA can directly detect events in texts without trigger words. We regard the event detection task of the chapter as a multi-label classification problem and apply the BLMA onto the application of detecting events in movie scripts. Experiment results show that BLMA performs well on an automatically tagged screenplay dataset containing 11 event types. F1 score of event detection using BLMA reaches 91.6% for a chapter with a single event, and 93.9% with multiple events, which outperforms other models. Therefore, our deep learning approach can effectively extract events from a chapter, which can be used for event detection on movie scripts. In addition, we explore the contribution of each component of the BLMA to the results, and find that multi-head attention has a significant impact on the performance. Keywords: Event Detection, Bi-LSTM, Attention.

Citation Download Citation

Weichuan Xu, Weiwen Zhang, and Depei Wang "Event detection without trigger words on movie scripts", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841G (10 November 2020); https://doi.org/10.1117/12.2577626

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