Extracting Qualitative Causal Structure with Transformer-Based NLP
This work addresses the challenge of automatically identifying causal structures in text for applications in domains like healthcare and science, representing an incremental advancement in NLP-based causal extraction.
The paper tackled the problem of extracting qualitative causal relationships from natural language using a transformer-based NLP architecture, achieving promising results in processing texts from academic publications, news articles, and social media.
Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express interactions between quantities (e.g., sleep decreases stress), between discrete events or entities (e.g., a protein inhibits another protein's transcription), or between intentional or functional factors (e.g., hospital patients pray to relieve their pain). This paper presents a transformer-based NLP architecture that jointly identifies and extracts (1) variables or factors described in language, (2) qualitative causal relationships over these variables, and (3) qualifiers and magnitudes that constrain these causal relationships. We demonstrate this approach and include promising results from in two use cases, processing textual inputs from academic publications, news articles, and social media.