J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2025 , Vol. 30 >Issue 5: 1065 - 1072

DOI: https://doi.org/10.1007/s12204-023-2675-y

Computing & Computer Technologies

Named Entity Identification of Chinese Poetry and Wine Culture Based on ALBERT

Expand
  • 1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 643002, Sichuan, China, 2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 643002, Sichuan, China

Received date: 2023-03-08

  Accepted date: 2023-05-10

  Online published: 2023-12-01

Fold

Abstract

The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system. Aimed at the characteristics of Chinese poetry and wine culture entities with different lengths and high training cost of named entity recognition models at the present stage, this study proposes a lite BERT+bi-directional long short-term memory+ attentional mechanisms +conditional random field (ALBERT+BILSTM+Att+CRF). The method first obtains the characterlevel semantic information by ALBERT module, then extracts its high-dimensional features by BILSTM module, weights the original word vector and the learned text vector by attention layer, and finally predicts the true label in CRF module (including five types: poem title, author, time, genre, and category). Through experiments on data sets related to Chinese poetry and wine culture, the results show that the method is more effective than existing mainstream models and can efficiently extract important entity information in Chinese poetry and wine culture, which is an effective method for the identification of named entities of varying lengths of poetry.

Cite this article

YANG Zhuang, LI Zhaofei, WANG Jihua, WEI Xudong, ZHANG Yijie . Named Entity Identification of Chinese Poetry and Wine Culture Based on ALBERT[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 1065 -1072 . DOI: 10.1007/s12204-023-2675-y

References

[1] LI J, SUN A X, HAN J L, et al. A survey on deep learning for named entity recognition [J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(1): 50-70.

[2] LIN Q K, MAO R, LIU J, et al. Fusing topology contexts and logical rules in language models for knowledge graph completion [J]. Information Fusion, 2023, 90: 253-264.

[3] CHEN H, HONG P F, HAN W, et al. Dialogue relation extraction with document-level heterogeneous graph attention networks [J]. Cognitive Computation, 2023, 15(2): 793-802.

[4] ZHANG D Q, YAO Y Z, LI J, et al. Synchronous condenser-based intelligent question answering system based on knowledge graph[M]//Proceedings of the world conference on intelligent and 3-D technologies (WCI3DT 2022). Singapore: Springer, 2023: 521-529.

[5] AONE C, HALVERSON L, HAMPTON T, et al. SRA: Description of the IE2 system used for MUC-7[C]//Seventh Message Understanding Conference. Fairfax: SRA: 1998.

[6] ETZIONI O, CAFARELLA M, DOWNEY D, et al. Unsupervised named-entity extraction from the Web: An experimental study [J]. Artificial Intelligence, 2005, 165(1): 91-134.

[7] HAN X, KWOH C K, KIM J J. Clustering based active learning for biomedical Named Entity Recognition [C]//2016 International Joint Conference on Neural Networks. Vancouver: IEEE, 2016: 1253-1260.

[8] WANG Z R, DU J, WANG J M. Writer-aware CNN for parsimonious HMM-based offline handwritten Chinese text recognition [J]. Pattern Recognition, 2020, 100: 107102.

[9] ALOKAILI A, EL BACHIR MENAI M. SVM ensembles for named entity disambiguation [J]. Computing, 2020, 102(4): 1051-1076.

[10] LIU S, HE T H, DAI J H. A survey of CRF algorithm based knowledge extraction of elementary mathematics in Chinese [J]. Mobile Networks and Applications, 2021, 26(5): 1891-1903.

[11] HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging [DB/OL]. (2015-08-09). https://arxiv.org/abs/1508.01991

[12] STRUBELL E, VERGA P, BELANGER D, et al. Fast and accurate entity recognition with iterated dilated convolutions [DB/OL]. (2017-02-07). https://arxiv.org/abs/1702.02098

[13] ULČAR M, ROBNIK-ŠIKONJA M. Cross-lingual alignments of ELMo contextual embeddings [J]. Neural Computing and Applications, 2022, 34(15): 13043-13061.

[14] SAVELKA J, AGARWAL A, BOGART C, et al. Large language models (GPT) struggle to answer multiple-choice questions about code [DB/OL]. (2023-03-09). https://arxiv.org/abs/2303.08033

[15] KUMAR P, RAMAN B. A BERT based dual-channel explainable text emotion recognition system [J]. Neural Networks, 2022, 150: 392-407.

[16] LAN Z Z, CHEN M D, GOODMAN S, et al. ALBERT: A lite BERT for self-supervised learning of language representations [DB/OL]. (2019-09-26). https://arxiv.org/abs/1909.11942

[17] AN Y, XIA X Y, CHEN X L, et al. Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF [J]. Artificial Intelligence in Medicine, 2022, 127: 102282.

Options
Outlines

/

〈 〉