Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns
Abstract
Existing approaches to causal embeddings rely heavily on hand-crafted high-precision causal patterns, leading to limited coverage. To solve this problem, this paper proposes a method to boost causal embeddings by exploring potential verb-mediated causal patterns. It first constructs a seed set of causal word pairs, then uses them as supervision to characterize the causal strengths of extracted verb-mediated patterns, and finally exploits the weighted extractions by those verb-mediated patterns in the construction of boosted causal embeddings. Experimental results have shown that the boosted causal embeddings outperform several state-of-the-arts significantly on both English and Chinese. As by-products, the top-ranked patterns coincide with human intuition about causality.
Cite
Text
Xie and Mu. "Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/266Markdown
[Xie and Mu. "Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/xie2019ijcai-boosting/) doi:10.24963/IJCAI.2019/266BibTeX
@inproceedings{xie2019ijcai-boosting,
title = {{Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns}},
author = {Xie, Zhipeng and Mu, Feiteng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {1921-1927},
doi = {10.24963/IJCAI.2019/266},
url = {https://mlanthology.org/ijcai/2019/xie2019ijcai-boosting/}
}