Abstract
The detection of textual Emotion-Cause Pair causality is very helpful for improving the accuracy of emotion-cause extraction and understanding the causes behind specific events. To solve the polysemy problem of static word vector representation in word2vec, an Emotion-Cause Pair causality discrimination model based on Bidirectional Encoder Representation from Transformers (BERT) is proposed. Firstly, each independent clause in the document is transformed into a word vector sequence by pretraining BERT, and the semantic representation of each independent clause is obtained by pooling. Secondly, the generated independent clause vector is used as the input of Bidirectional Long Short-Term Memory (BiLSTM) or SelfAttention, and then the deep semantic representation of the relevant independent clause context is obtained. Finally, the feature vectors extracted in the previous two stages are subjected to multi-target weighted fusion correction and input to the fully connected layer, and the maximum probability label sequence is calculated by the Softmax function to achieve causality detection. The experimental results show that compared with the baseline model, the Recall value of the proposed method is increased by 9.01%, and the F1 value is increased by 2.71%.
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This research was supported by General Project of Graduate Research and Innovation of Huzhou University (2020KYCX24).
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Cao, Q., Jnr.Asiedu, C., Hao, X. (2022). Research on the Detection of Causality for Textual Emotion-Cause Pair Based on BERT. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_48
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