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

Manik Bhandari, Mark Feblowitz, Oktie Hassanzadeh, Kavitha Srinivas, Shirin Sohrabi

AAAI 2021 pp. 15759-15760

doi:10.1609/AAAI.V35I18.17876 /aaai/2021/bhandari2021aaai-unsupervised/

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 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.

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Cite

Text

Bhandari et al. "Unsupervised Causal Knowledge Extraction from Text Using Natural Language Inference (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17876

Markdown

[Bhandari et al. "Unsupervised Causal Knowledge Extraction from Text Using Natural Language Inference (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/bhandari2021aaai-unsupervised/) doi:10.1609/AAAI.V35I18.17876

BibTeX

@inproceedings{bhandari2021aaai-unsupervised,
  title     = {{Unsupervised Causal Knowledge Extraction from Text Using Natural Language Inference (Student Abstract)}},
  author    = {Bhandari, Manik and Feblowitz, Mark and Hassanzadeh, Oktie and Srinivas, Kavitha and Sohrabi, Shirin},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15759-15760},
  doi       = {10.1609/AAAI.V35I18.17876},
  url       = {https://mlanthology.org/aaai/2021/bhandari2021aaai-unsupervised/}
}