Text understanding and common sense learning from narrative texts
Document Type
Conference Proceeding
Publication Date
1-1-2020
Abstract
In this work, we develop a hybrid SVM / rule-based classification method for identification of scene-level contexts and subsequent extraction of temporal, spatial, and causal relations from within and between these contexts within narrative-style texts (such as novels and investigative news stories). We also develop methods for generalizing from narratives to larger contexts - aka, "common sense" type knowledge. Knowledge extraction results are compared against gold standard human annotation of a small dataset of 20 narrative stories across a mix of genre (news, fiction, non-fiction).
Publication Source (Journal or Book title)
Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016
First Page
2098
Last Page
2103
Recommended Citation
Khazaeli, S., & Knapp, G. (2020). Text understanding and common sense learning from narrative texts. Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016, 2098-2103. Retrieved from https://repository.lsu.edu/mechanical_engineering_pubs/1468