Network Business Intention Classification and Slot Filling Method Based on LC-BERT
Pages 284 - 288
Abstract
With the development of new network structures, how to quickly understand the network intentions expressed by users in natural language is very important. Aiming at the problem of feature sparseness and semantic ambiguity caused by the limited number of words in the network business text, a method dealing with the joint intention classification and slot filling LC-BERT is proposed. The position vector, original word vector, and segmentation vector are used as the input of BERT, and the long-distance dependency is obtained through the multi-head self-attention mechanism, and the global semantic features and word vectors are extracted. The LDA topic model is used to extend the feature representation method of short texts to solve the problem of sparse data and lack of subject information. The experimental results show that the LC-BERT model has better classification and slot filling effects than traditional models, proving this method's feasibility and effectiveness.
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Index Terms
- Network Business Intention Classification and Slot Filling Method Based on LC-BERT
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Published In
December 2022
365 pages
ISBN:9781450398039
DOI:10.1145/3579895
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 04 April 2023
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ICNCC 2022
ICNCC 2022: 2022 The 11th International Conference on Networks, Communication and Computing
December 9 - 11, 2022
Beijing, China
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