Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)

In this paper, we address the problem of extracting causal knowledge from text documents in a weakly supervised manner. We target use cases in decision support and risk management, where causes and effects are general phrases without any constraints. We present a method called CaKNowLI which only ta...

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Bibliographic Details
Published in:Proceedings of the ... AAAI Conference on Artificial Intelligence 2021-05, Vol.35 (18), p.15759-15760
Main Authors: Bhandari, Manik, Feblowitz, Mark, Hassanzadeh, Oktie, Srinivas, Kavitha, Sohrabi, Shirin
Format: Article
Language:English
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Summary:In this paper, we address the problem of extracting causal knowledge from text documents in a weakly supervised manner. We target use cases in decision support and risk management, where causes and effects are general phrases without any constraints. We present a method called CaKNowLI which only takes as input the text corpus and extracts a high-quality collection of cause-effect pairs in an automated way. We approach this problem using state-of-the-art natural language understanding techniques based on pre-trained neural models for Natural Language Inference (NLI). Finally, we evaluate the proposed method on existing and new benchmark data sets.
ISSN:2159-5399
2374-3468
DOI:10.1609/aaai.v35i18.17876