LeakGAN-Based Causality Extraction in the Financial Field

Tenth International Conference on Applications and Techniques in Cyber Intelligence (ICATCI 2022)(2023)

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Abstract
Causality extraction model can quickly extract causality in text. It can be applied to event prediction, question-answering systems, and scenario generation. The traditional causality extraction pays more attention to the extraction of entities and ignores the deep semantic representation between cause entities and effect entities of the text. So the accuracy of causality extraction is low in the end. A causal relationship extraction model based on LeakGAN is proposed to solve this problem. The core task of the model is to analyze the causality existing in the review text ( this paper focuses on explicit one-cause and one-effect analysis). It also realizes deep extraction under semantic enhancement. Four levels of causality extraction model based on LeakGAN are proposed: data preprocessing, pre-training, Bi-LSTM + Attention, and LeakGAN learning. Experimental results show that the model can improve the accuracy of causality extraction.
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Key words
LeakGAN, Bi-LSTM + Attention, Causal relation extraction
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