Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210800197-5.doi: 10.11896/jsjkx.210800197

• Artificial Intelligence • Previous Articles     Next Articles

Customs Synonym Recognition Fusing Multi-level Information

LIU Da-wei1, CHE Chao1, WEI Xiao-peng1,2   

  1. 1 Key Laboratory of Advanced Design and Intelligent Computing,Ministry of Education,Dalian University,Dalian 116622,China
    2 School of Computer Science and Technology,Dalian University of Technology,Dalian 116081,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:LIU Da-wei,born in 1993,postgradua-te.His main research interests include synonym recognition and so on.
    WEI Xiao-peng,Ph.D,professor.His main research interests include medical and health informatics,computer animation,computer vision,and intelligent CAD.
  • Supported by:
    National Natural Science Foundation of China(61877008,62076045).

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Abstract: In the text information of customs import and export commodities,different words are often used to describe the chara-cteristics of the same commodity.Recognizing the characteristic synonyms of these commodities can better summarize opinions,and then prevent and control the tax-related risks for commodities with the same characteristics.According to the characteristics of phrases of customs declaration elements,a convolution neural network model fusing multi-level information is proposed,and a Sentence-BERT based on twin and three-level network structure is constructed and trained,which has a better semantic representation of similar element phrases,and makes up for the shortage of discrete and sparse embedded features of short text words in Word2Vec.Multi-size convolution kernel is used to extract different features of keywords.The BiLSTM neural network is used to learn the word order information of element phrases,and the attention mechanism is used to assign the weight of keywords.The full connection layer integrates semantic features of synonyms and keyword features,and is predicted by SoftMax layer.Experiments show that the convolution model fusing multi-level information has better performance than other models.

Key words: Customs commodity, Synonym recognition, Element phrases, Multi-level information, Convolution neural network

CLC Number: 

  • TP391
[1]FEI H,TAN S,LI P.Hierarchical multi-task word embedding learning for synonym prediction[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Disco-very & Data Mining.2019:834-842.
[2]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-Training of Deep Bidirectional Transformers for Language Understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics.Minneapolis,Minnesota,2019:4171-4186.
[3]CHANG W C,YU F X,CHANG Y W,et al.Pre-training tasks for embedding-based large-scale retrieval [C]//Proceedings of the 8th International Conference on Learning Representations(ICLR).2020.
[4]REIMERS N,GUREVYCH I.Sentence-Bert:Sentence Embeddings Using Siamese Bert-Networks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.2019:3973-3983.
[5]HASAN S A,LIU B,LIU J,et al.Neural clinical paraphrasegeneration with attention[C]//Proceedings of the Clinical Na-tural Language Processing Workshop(ClinicalNLP).2016:42-53.
[6]ZHAO Y,LIU Q,HU F,et al.Synonym Extraction in Shipping Industry with Distant Supervision and Deep Neural Network [J].Journal of Coastal Research,2019,94(sp1):455-459.
[7]ZHANG Y,ROLLER S,WALLACE B.MGNC-CNN:A simple approach to exploiting multiple word embeddings for sentence classification [C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:1522-1527
[8]YU W,ZHENNI Z,JIE Y,et al.Weibo Sentiment Classification Based on Two Channels Text Convolution Neural Network with Multi-Feature [C]//2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery(CyberC).2020:152-60.
[9]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in VectorSpace[C]//Proceedings of the 1st International Conference on Learning Representations.Scottsdale,USA,2013.
[10]PENNINGTON J,SOCHER R,MANNING C D.Glove:Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing(EMNLP).2014:1532-1543.
[11]LAI S,XU L,LIU K,et al.Recurrent convolutional neural networks for text classification[C]//Proceedings of the Twenty-ninth AAAI Conference on Artificial Intelligence.2015:2267-2273
[12]ZHOU P,SHI W,TIAN J,et al.Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.2016:207-212.
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