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| Extracting Financial Events with ELECTRA and Part-of-Speech |
Chen Xingyue,Ni Liping( ),Ni Zhiwei |
School of Management, Hefei University of Technology, Hefei 230009, China
Key Laboratory of Process Optimization & Intelligent Decision-making, Ministry of Education, Hefei University of Technology, Hefei 230009, China |
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Abstract [Objective] This paper proposes a method to extract financial events based on the ELECTRA model and part-of-speech, aiming to address the issues of blurred entity boundaries and inaccurate extractions. [Methods] First, we input corpus to two models pre-trained by ELECTRA, which identified key entities, the original semantic information, and part-of-speech. Then, we used the BiGRU model to extract contextual semantic dependency and generated the original sequence tags. Finally, we addressed the issues of label deviation with the CRF model and extracted the financial events. [Results] We examined the new model with financial event dataset and found its F-value reached 70.96%, which was 20.74 percentage point higher than the BiLSTM-CRF model. [Limitations] The number of events in the dataset needs to be increased. The size of pre-trained model is large, which might be limited by the memory of GPU/TPU. [Conclusions] The model based on ELECTRA and part-of-speech could effectively identify the relationships among financial events to extract them.
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Received: 26 December 2020
Published: 19 April 2021
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| Fund:National Natural Science Foundation of China(71301041);National Natural Science Foundation of China(91546108);National Natural Science Foundation of China(71701061) |
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Corresponding Authors:
Ni Liping,ORCID：0000-0002-7067-302X
E-mail: niliping@hfut.edu.cn
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