Sports news relationship extraction based on BERT's BiLSTM and attention

Yuan Zhao; Yingyun Yang

doi:10.1117/12.2628524

22 April 2022 Sports news relationship extraction based on BERT's BiLSTM and attention

Yuan Zhao, Yingyun Yang

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 1217418 (2022) https://doi.org/10.1117/12.2628524
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

With the development of Internet big data, how people obtain a large amount of news text information and automatically obtain the information they want from the text information is an urgent task. In order to structure and analyze a large amount of Chinese text information on the Internet, this paper proposes an entity extraction method based on the BERT pre-training model and BiLSTM with the Attention Mechanism. Aiming at the problem that the BiLSTM model can only obtain feature information at the sentence context level, but cannot obtain local feature information. In this paper, based on the BiLSTM model, a BERT feature extraction model is added to obtain word vectors containing contextual semantic information, thereby capturing global and local information. At the same time, an Attention Mechanism is added to improve the effect of the model. The model was trained on the 2018 Football World Cup dataset corpus, and it was verified that the precision, F1 value and recall rate of the model have significantly improved performance on the dataset.

Citation Download Citation

Yuan Zhao and Yingyun Yang "Sports news relationship extraction based on BERT's BiLSTM and attention", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 1217418 (22 April 2022); https://doi.org/10.1117/12.2628524

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