OperaMiner: Extracting Character Relations from Opera Scripts Using Deep Neural Networks

Abstract

Retrieving character relations from opera scripts helps performers and audience accurately understand the features and behavior of roles. Meanwhile, discovering the evolution of character relations in an opera benefits many opera-oriented story exploration tasks. Aiming to automatically extract relations among opera characters, we demonstrate a prototype system named OperaMiner, which extracts relations for opera characters based on a hybrid deep neural network. The major features of OperaMiner are: (1) It provides a uniform reasoning framework for character relations considering language structure information as well as explicit and implicit expressions in opera scripts. (2) It explores the deep features in opera scripts, including character embeddings features, word embeddings features, and the linguistic features in artistic texts. (3) It presents a hybrid learning architecture enhancing CNN and Bi-LSTM with a CRF layer for character relation extraction. After a brief introduction to the architecture and key technologies of OperaMiner, we present a case study to demonstrate the main features of OperaMiner, including the generation of the character relation graph, the demonstration of major roles, and the evolution sequence of character relations.