Identifying Predictive Causal Factors from News Streams

Ananth Balashankar, Sunandan Chakraborty, Samuel Fraiberger, Lakshminarayanan Subramanian


Abstract
We propose a new framework to uncover the relationship between news events and real world phenomena. We present the Predictive Causal Graph (PCG) which allows to detect latent relationships between events mentioned in news streams. This graph is constructed by measuring how the occurrence of a word in the news influences the occurrence of another (set of) word(s) in the future. We show that PCG can be used to extract latent features from news streams, outperforming other graph-based methods in prediction error of 10 stock price time series for 12 months. We then extended PCG to be applicable for longer time windows by allowing time-varying factors, leading to stock price prediction error rates between 1.5% and 5% for about 4 years. We then manually validated PCG, finding that 67% of the causation semantic frame arguments present in the news corpus were directly connected in the PCG, the remaining being connected through a semantically relevant intermediate node.
Anthology ID:
D19-1238
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2338–2348
Language:
URL:
https://aclanthology.org/D19-1238
DOI:
10.18653/v1/D19-1238
Bibkey:
Cite (ACL):
Ananth Balashankar, Sunandan Chakraborty, Samuel Fraiberger, and Lakshminarayanan Subramanian. 2019. Identifying Predictive Causal Factors from News Streams. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2338–2348, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Identifying Predictive Causal Factors from News Streams (Balashankar et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1238.pdf