Script2Graph: Auto-construct Screenplay Text World by Mining Contextual Information
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
2023 3rd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2023
DOI
10.1109/SEAI59139.2023.10217409
First Page
288
Last Page
293
Abstract
With the rapid development of artificial intelligence and the continuous emergence of innovative technologies, its application in movie scenes has gained attention. Meanwhile, the application of natural language processing techniques in movie scripts has gradually attracted the attention of researchers, making text information extraction more intelligent and convenient. However, most of the previous works for unstructured script processing ignore the theoretical guidance of computational language and lack the framework of knowledge schema. Therefore, we propose Script2Graph method based on text world theory to solve unstructured text information extraction and knowledge organization, and realize the automatic construction of script text world. Our experiments demonstrate that the method is effective in extracting text world elements and using high-quality information in downstream applications.
Funding Number
2020B1212060069
Funding Sponsor
National Natural Science Foundation of China
Keywords
computational linguistics, information extraction, knowledge triplet graph, natural language processing, Text world theory
Department
Industrial and Systems Engineering
Recommended Citation
Depei Wang, Lianglun Cheng, Hongwei Liu, Ruihao Li, Huilin Wang, and Hongrui Liu. "Script2Graph: Auto-construct Screenplay Text World by Mining Contextual Information" 2023 3rd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2023 (2023): 288-293. https://doi.org/10.1109/SEAI59139.2023.10217409
