Opera-oriented character relations extraction for role interaction and behaviour Understanding: a deep learning approach

Xinnan Dai, Xujian Zhao, Peiquan Jin, Xuebo Cai, Hui Zhang, Chunming Yang and Bo Li

Behaviour and Information Technology, 2019, vol. 38, issue 9, 900-912

Abstract: There are a great number of complex relations among different characters in an opera. Retrieving such relations is crucial for performers and audience to accurately understand the features and behaviour of roles. Aiming to automatically extract relations among characters in an opera, in this paper we propose an effective method that can extract character relations from opera scripts. Firstly, we construct a uniform reasoning framework for opera scripts. Based on this model, we propose a deep syntax-parsing method to detect character relations from opera scripts. After that, we propose a new deep learning approach called SL-Bi-LSTM-CRF to extract the objects involved in character relations. The proposed SL-Bi-LSTM-CRF algorithm is a sentence-level relation extraction algorithm based on the Bi-directional LSTM with a CRF layer. With this mechanism, we are able to get a detailed description for character relations. We conduct experiments on a real dataset of opera scripts. The experimental results in terms of precision, recall, and F-score suggest the effectiveness of our proposal.

Date: 2019
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DOI: 10.1080/0144929X.2019.1584246

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