학술논문

What Causes Unemployment?: Unsupervised Causality Mining from Swedish Governmental Reports
Document Type
Author
Source
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023). :25-29
Subject
causality mining
unsupervised approaches
Datorlingvistik
Computational Linguistics
Language
English
Abstract
Extracting statements about causality from text documents is a challenging task in the absence of annotated training data. We create a search system for causal statements about user-specified concepts by combining pattern matching of causal connectives with semantic similarity ranking, using a language model fine-tuned for semantic textual similarity. Preliminary experiments on a small test set from Swedish governmental reports show promising results in comparison to two simple baselines.