Extraction of Causal Relations in Text
Date:
I gave a 3-hours tutorial about extracting causal relations from text.
Abstract: Causality describes a relationship between two entities or events, one of which is an Effect resulting from a Cause. Causality is a core cognitive ability of humans and important for many scientific fields. Causal Relation Extraction (CRE) aims to extract relations that exhibit causal meaning. Some tasks in CRE include identifying if a causal relation exists in the sentence, or at the finer-grained level, which words refer to the Cause and Effect event. These are challenging tasks since they tackle a deeper level of machine understanding, closer to human’s abilities. CRE is also important because it has many applications, like for summarization, concept extraction, causal knowledge identification for downstream NLP tasks (e.g. Question Answering) and for causal reasoning in NLP. In this workshop, we will provide a brief introduction to the tasks, datasets, and models of CRE, assuming only general machine learning background and ideally some prior exposure to NLP.