A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

TANG Jinjun; TUO Haonan; LIU You; FU Qiang

doi:10.3963/j.jssn.1674-4861.2022.05.009

Volume 40 Issue 5

Nov. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(5): 80-90

TANG Jinjun, TUO Haonan, LIU You, FU Qiang. A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 80-90. doi: 10.3963/j.jssn.1674-4861.2022.05.009

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TANG Jinjun, TUO Haonan, LIU You, FU Qiang. A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 80-90. doi: 10.3963/j.jssn.1674-4861.2022.05.009

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A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

doi: 10.3963/j.jssn.1674-4861.2022.05.009

School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Received Date: 2022-01-02
Available Online: 2022-12-05

Abstract

Abstract

Autonomous Transportation System (ATS) consists of participants whose information is generally described by texts. In order to develop a knowledge graph of the participants of the ATS, it is necessary to accurately identify the participants from the texts. Therefore, an entity recognition method based on a BERT-Bi-LSTM-CRF model is developed to extract the participants of ATS. Specifically, a Bi-LSTM (bidirectional long short-term memory) model is used to bi-directionally extract contextual sequence information from the semantic characteristics, which are captured by a word embedding model—BERT (bidirectional encoder representation from transformers). The optimal results of sequence prediction are obtained through the CRF(conditional random fields). After the original text source related to transportation engineering is collected, preprocessed and annotated, a new dataset is developed for identifying the participants of the ATS. Moreover, a comparative experiment of the entity recognition is carried out based on the same dataset. The results indicate that the BERT model significantly improves the performance of identifying the participants. Compared with other methods such as CNN-LSTM and Bi-LSTM, the proposed method achieves the best performance. The overall F1-score of participants is 86.81%, which shows that the proposed BERT model can enhance the generalized capability of the detection methods by extracting the semantic features of participants. The for identifying each type of including "user" "operator" "supplier" "planner" and "maintainer" reaches 90.35%, 92.31%, 90.48%, 93.33%, and 95.00%, respectively. Therefore, it can be concluded from the study results that the proposed method is effective and accurate.
- intelligent transportation,
- named entity recognition,
- knowledge graph,
- BERT-Bi-LSTM-CRF,
- knowledge modeling

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References(26)

References

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A Method for Identifying the Participants of Autonomous Transportation System Based on a BERT-Bi-LSTM-CRF Model

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