Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1387-1395.doi: 10.13229/j.cnki.jdxbgxb20200358

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Korean text structure discovery based on reinforcement learning and attention mechanism

Ya-hui ZHAO(),Fei-yang YANG,Zhen-guo ZHANG(),Rong-yi CUI   

  1. Deptartment of Computer Science & Technology，Yanbian University，Yanji 133002，China
  • Received:2020-05-22 Online:2021-07-01 Published:2021-07-14
  • Contact: Zhen-guo ZHANG E-mail:yhzhao@ybu.edu.cn;zgzhang@ybu.edu.cn

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Abstract:

In this paper, attention mechanism is combined with deep reinforcement learning, and label information is used to study how to learn effective Korean language text structured representation independently. Two structured representation models are proposed, which are called Information Distilled Attention (IDA) and Hierarchically Structured Attention (HSA). IDA selects the important words related to the task, and HSA finds the phrase structure in the sentence. The structural discovery in both presentation models is a sequential decision problem that can be implemented using Policy Gradient (PG) in reinforcement learning. The experimental results show that the proposed IDA can recognize the important words of Korean, and HSA can extract the sentence structure well, and have good performance in the task of text classification. At the same time, the results of the two models have a good auxiliary effect on corpus tagging.

Key words: artificial intelligence, deep reinforcement learning, attention mechanism, text structure discovery, Korean natural language processing

CLC Number: 

  • TP391.1

Fig.1

Soft attention"

Fig.2

Network model structure diagram based on reinforcement learning"

Fig.3

ID-Attention model"

Fig.4

HS-Attention model"

Table 1

Details of Korean text dataset"

类别词条数类别词条数
动物4582植物6172
微生物5472生物技术1215
生物医学2752气候708
海洋环境810地质1735
海洋技术819材料工程781
测控技术1728航天4436
其他1478

Fig.5

Training process of ID-Attention and HS-Attention"

Fig.6

Changes in HS-Attention loss function value"

Table 2

Accuracy under different classifiers models"

模型准确率/%模型准确率/%
CNN78.50Self?Attention82.91
LSTM74.60ID?LSTM82.11
Bi?LSTM78.14HS?LSTM83.65
MT76.50ID?Attention84.84
T?BLSTM?CNN81.68HS?Attention85.11

Table 3

Examples of structures distilled and discovered by ID-Attention and HS-Attention"

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ID?Attention??? ?????????????? ??? ?? ?? ?? ???? ?? ?????????????????? ??? ?????
HS?Attention??? | ?? ??? ??? ?? ?? ?? | ??? ?? ?? | ?? ???? ?? ??? ?? ? ??? ?? ???? | ??? ??? ??? ?? |
原始文本?? ????? ?? ??? ???? ?? ? ????? ?? ?? ???? ???? ??? ??? ?? ??? ? ??（从昆虫病原直接取样，检出及定量化可直接反映昆虫流行病学调查中病原丰度）
ID?Attention?? ?????????? ???? ???????????? ??????????? ???????????
HS?Attention?? ????? | ?? ??? ???? ?? ? ????? | ?? ?? ???? ???? ??? | ??? ?? ??? ? ?? |
原始文本???? ?? ???? ? ?? ?? ??? ???? ????? ?? ?? ???? ???? ?? ?? ?? ?? ???? ???? ?? ??? ????? ??? ??（温室气体排放清单是目前计算城市碳排放量最常用的机理，并有助于在不同的产业或领域研究温室气体排放现状）
ID?Attention?????????? ????? ??? ???? ????????????????????????? ???? ???? ?????????? ??? ??
HS?Attention???? ?? ???? ? | ?? ?? ??? ???? | ????? ?? ?? ???? ???? | ?? ?? ?? ?? ???? | ???? ?? ??? ????? | ??? ??
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