Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Deep Structured Learning for Natural Language Processing

Deep Structured Learning for Natural Language Processing The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) Association for Computing Machinery

Deep Structured Learning for Natural Language Processing

Download PDF
14 pages

Loading next page...
 
/lp/association-for-computing-machinery/deep-structured-learning-for-natural-language-processing-6I9Pg6Uq50

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Association for Computing Machinery
Copyright
Copyright © 2021 Association for Computing Machinery.
ISSN
2375-4699
eISSN
2375-4702
DOI
10.1145/3433538
Publisher site
See Article on Publisher Site

Abstract

The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.

Journal

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) – Association for Computing Machinery

Published: Jul 14, 2021

Keywords: Food safety

References

  • Comparative analysis of information dissemination features and efficiency of social network of universities based on micro-blog and WeChat platforms
    Liu, H.; Li, Y.; Sun, J. J.
  • and University J
    Huang, W.; Liu, Y. J.; Wang, J. J.; Han, R. X.; Management, S. O.
  • When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs
    Cheng, G.; Yang, C. Y.; Yao, X. W.; Li, K. M.; Han, J. W.
  • Sequence discriminative training for deep learning based acoustic keyword spotting
    Chen, Z. H.; Qian, Y. M.; Yu, K.
  • A survey on deep learning for big data
    Zhang, Q. C.; Yang, L. T.; Chen, Z. K.; L., P.
  • Design and implementation of hadoop based storage and management platform on the weibo public opinion
    Yu, H.; Huang, Y. F.; Hu, P.
  • Research on chinese event extraction
    Zhao, Y. Y.; Qin, B.; Che, W. X.; Liu, T.
  • Research on automatic generation of extraction patterns
    Zhang, J. H.; Wang, X. Y.; Li, F.
  • A hierarchical distance-dependent bayesian model for event coreference resolution
    Yang, B. S.; Cardie, C.; Frazier, P. I.
  • Multilingual event extraction for epidemic detection
    Lejeune, G.; Brixtel, R.; Doucet, A.; Lucas, N.
  • Learning document-level semantic properties from free-text annotations
    Branavan, S. R. K.; Chen, H.; Eisenstein, J.; Barzilay, R.
  • Spatial role labeling: Towards extraction of spatial relations from natural language
    Kordjamshidi, P.; Otterlo, M. V.; Moens, M. F.
  • Relational learning for spatial relation extraction from natural language
    Kordjamshidi, P.; Frasconi, P.; Otterlo, M. V.; Moens, M. F.; Raedt, L. D.
  • A semi-supervised learning framework for biomedical event extraction based on hidden topics
    Zhou, D. Y.; Zhong, D. Y.
  • Neural architectures for named entity recognition
    Lample, G.; Ballesteros, M.; Subramanian, S.; Kawakami, K.; Dyer, C.
  • Research on the evolution mechanism and trend prediction of netizens for public opinion big data
    Lan, Y. X.; Xia, Y. X.; Liu, B. Y.; Gao, Y.; Li, Z.
  • Document sentiment orientation analysis based on sentence weighted algorithm
    Li, A. P.; Di, P.; Duan, L. G.
  • Sentiment analysis of chinese micro blog based on lexicon and rule set
    Wang, Z. T.; Yu, Z. W.; Guo, B.; Lu, X. J.
  • A sentiment analysis system to improve teaching and learning
    Rani, S.; Kumar, P.
  • A neural word embeddings approach for multi-domain sentiment analysis
    Dragoni, M.; Petrucci, G.
  • Deep learning model for multi-dimensional analysis of internet public opinion in chinese text environment
    Tan, X.; Wu, J. J.; Xu, L.; Mao, T. T.
  • Neural machine translation by jointly learning to align and translate
    Dzmitry, B.; Kyunghyun, C.; Yoshua, B.