Causal graph extraction from news: a comparative study of time-series causality learning techniques.

ABSTRACT: Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles. This article presents a novel framework for extracting causal graphs from digital text media. The framework relies on topic-relevant variables representing terms and ongoing events that are selected from a domain under analysis by applying specially developed information retrieval and natural language processing methods. Events are represented as event-phrase embeddings, which make it possible to group similar events into semantically cohesive clusters. A time seri

SUBMITTER: Maisonnave M 

PROVIDER: S-EPMC9374167 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature