Paper
28 April 2023 Research on intention tendency detection for Chinese medical question answering task
Musheng Chen, Ying Liu, Junhua Wu
Author Affiliations +
Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126102B (2023) https://doi.org/10.1117/12.2671431
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China
Abstract
In the Chinese medical question and answer task, question intention detection is a very important part. At present, the common intention detection methods mainly use the manually designed matching rules to find the problem features to detect the intention of the problem, but the use of a large amount of labor usually brings about problems such as high cost and poor versatility. A novel method of intention detection is proposed in this paper. First, the collected questions with different intention categories are used to construct intention feature words. Then, based on the BERT pre-training language model, a two-classification model of phrase similarity is constructed. By comparing the combination results of problem word segmentation and the similarity of intention feature words, the multi-classification problem of problem intention detection is transformed into a two-classification problem between multiple phrases. Then we can get the inclination of the question for each intention category, that is the intention category of the question. The experiment shows that the method based on the two-classification model of phrase similarity has better effect than the previous methods, and the F1 value in the test set reaches 90.1.
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Musheng Chen, Ying Liu, and Junhua Wu "Research on intention tendency detection for Chinese medical question answering task", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126102B (28 April 2023); https://doi.org/10.1117/12.2671431
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KEYWORDS
Binary data

Deep learning

Design rules

Semantics

Education and training

Medical research

Data modeling

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