Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings

@article{Li2019CausalityEB,
  title={Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings},
  author={Zhaoning Li and Qi Li and Xiaotian Zou and Jiangtao Ren},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.07629}
}
Causality extraction from natural language texts is a challenging open problem in artificial intelligence. Existing methods utilize patterns, constraints, and machine learning techniques to extract causality, heavily depend on domain knowledge and require considerable human efforts and time on feature engineering. In this paper, we formulate causality extraction as a sequence tagging problem based on a novel causality tagging scheme. On this basis, we propose a neural causality extractor with… CONTINUE READING
2
Twitter Mentions

References

Publications referenced by this paper.
SHOWING 1-10 OF 50 REFERENCES

Deep contextualized word representations

VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers