Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)

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Electric Circuits Course Knowledge Named Entity Recognition Based on Enhanced Word Embedding

Authors
Nan Wang1, *, Dong Liang1, Ruolin Dou1
1Wireless Signal Processing and Network Lab, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
*Corresponding author. Email: wangnan0123@foxmail.com
Corresponding Author
Nan Wang
Available Online 26 September 2023.
DOI
10.2991/978-94-6463-238-5_89How to use a DOI?
Keywords
electric circuits course; named entity recognition; feature vector; enhanced word embedding
Abstract

This paper conducts named entity recognition research on electric circuits course knowledge, realizing entity extraction from unstructured text data in a specific field, aiming to achieve the effective utilization of subject knowledge data. This paper proposes a word embedding enhanced BiLSTM-CRF model. Feature vectors based on the keyword dictionary of electric circuits course are constructed to make better use of domain text information. Word embedding enhancement includes two aspects. One is static feature vectors, which are composed of static word embedding, POS feature vectors, and feature vectors combined with keyword dictionary. The other is context word embedding, which uses an attention mechanism to fuse static feature vectors and context word embedding. Through comparative experiments and result analysis, the F1 score of the model with enhanced word embedding has increased by 3.18%, proving the effectiveness of using static feature vectors and contextual word embedding.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
Series
Advances in Intelligent Systems Research
Publication Date
26 September 2023
ISBN
978-94-6463-238-5
ISSN
1951-6851
DOI
10.2991/978-94-6463-238-5_89How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nan Wang
AU  - Dong Liang
AU  - Ruolin Dou
PY  - 2023
DA  - 2023/09/26
TI  - Electric Circuits Course Knowledge Named Entity Recognition Based on Enhanced Word Embedding
BT  - Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
PB  - Atlantis Press
SP  - 679
EP  - 686
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-238-5_89
DO  - 10.2991/978-94-6463-238-5_89
ID  - Wang2023
ER  -