Abstract
The flight training comments are textual evaluation from the instructor to the flight training students. Extracting the assessment experience and knowledge from the flight training comments can help improve the flight training level. To this end, a joint extraction framework is proposed in this paper. For the model input, we use an end-to-end grid tagging scheme as the decoding algorithm to extract aspect terms, opinion terms and opinion pairs simultaneously. Considering the continuity of terms, we propose WoBERT as encoder, which modifies the pre-trained word segmentation based on BERT, and realizes Chinese word segmentation at word-level granularity. We conduct extensive experiments on a real flight training comments dataset. The results show that the model with WoBERT as the encoder achieves the best performance with a F1-score of 0.647, which can be used for the task of knowledge extraction from the flight training comments.
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Acknowledgements
This work was supported in part by: National Natural Science Foundation of China (Nos. U2033213, 61966008, 61873042), Open Found of Key Laboratory of Flight Techniques and Flight Safety, CAAC (Nos. FZ2021KF01, FZ2021KF14).
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Zhang, Y., Shang, J., Zheng, L., Wu, Q., Cao, W., Sun, H. (2023). A Joint Framework for Knowledge Extraction from Flight Training Comments. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_2
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DOI: https://doi.org/10.1007/978-981-99-2356-4_2
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