Abstract
Causality forms the basis for reasoning and decision-making in artificial intelligence systems. To take advantage of the vast amount of textual data available today, causal discovery from text has become a significant challenge in recent years. Text data contains rich contextual semantic information. However, traditional causal discovery methods only handle structured data and do not consider serial relationships and semantic relevance between words on textual variables. To address this problem, in this paper, we propose a causal discovery method Text Causal Discovery Based on Sequence Structure Information (TCDSS) discovers strongly correlated text word pairs with semantic relevance and statistical causality and finally constructs lexical causal graphs by introducing sequence-structure information in the causal discovery algorithm. We tested our method TCDSS on the DXY-COVID-19-Data and the Chinese Emergency Corpus (CEC) and compared it with other existing causal discovery methods. The experimental results show that PC, IGCI, RECI, and other forms have improved in precision, recall, and structural Hamming distance (SHD) after the introduction of TCDSS.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 62076210, No. 81973752), the Natural Science Foundation of Xiamen city (No. 3502Z20227188) and the Open Project Program of The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions, Wuyi University(No. KLCCIIP2020203).
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Li, Y., Cao, D., Lin, D. (2024). Text Causal Discovery Based on Sequence Structure Information. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_13
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DOI: https://doi.org/10.1007/978-981-99-8540-1_13
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