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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 11
3310-3315

Soft prototype enhanced adaptive loss model for aspect extraction

Xu Fu
Huang Xianying
Jiang Xingyu
Peng Jingyao
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0101

Abstract

Aspect extraction is a core task of aspect-level sentiment analysis. The current method is to use aspect extraction, opinion extraction, and the relationship between aspect-level sentiment classification to construct a multi-relationship collaborative learning model. Commonly data sets have problems such as fewer occurrences of most aspect words and context words, lack of sample exposure, etc., which makes the sequence tagger converge to a very poor area, and because different parts of the pair are ignored when calculating the total loss of the model. The difference in extraction effect makes the neural network model almost unable to achieve the best performance. Therefore, this paper proposed a soft prototype enhanced adaptive loss model(SPEAL) for aspect extraction, and established the dynamic relationship between low sample exposure text and high sample exposure text through soft retrieval. At the same time, it was based on aspect extraction, opinion extraction, and aspect level. The contribution of sentiment classification to the aspect extraction adaptively updated the weight of each part of the loss. The experimental results on the three data sets(REST14, REST15, and LAP14) show that SPEAL accelerates the convergence while improving the effect of aspect extraction.

Foundation Support

国家自然科学基金资助项目(17XXW005)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0101
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 11
Section: Algorithm Research & Explore
Pages: 3310-3315
Serial Number: 1001-3695(2021)11-019-3310-06

Publish History

[2021-11-05] Printed Article

Cite This Article

徐福, 黄贤英, 蒋兴渝, 等. 用于方面提取的软原型增强自适应损失模型 [J]. 计算机应用研究, 2021, 38 (11): 3310-3315. (Xu Fu, Huang Xianying, Jiang Xingyu, et al. Soft prototype enhanced adaptive loss model for aspect extraction [J]. Application Research of Computers, 2021, 38 (11): 3310-3315. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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