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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 118-126    DOI: 10.11925/infotech.2096-3467.2019.0907
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Identifying Scenic Spot Entities Based on Improved Knowledge Transfer
Zhao Ping1,Sun Lianying2(),Tu Shuai1,Bian Jianling3,Wan Ying1
1Smart City College, Beijing UnionUniversity, Beijing 100101, China
2College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China
3Beijing China-Power Information Technology Co., LTD, Beijing 100192, China
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Abstract  

[Objective] This paper addresses the issues facing labeled data in the recognition of scenic spots.[Methods] We proposed an improved knowledge transfer algorithm for entity recognition and used datasets from the People’s Daily to evaluate our new model.[Results] Our method’s accuracy was 1.62% higher than the model using all labeled data.[Limitations] More research is needed to examine the expansion of samples.[Conclusions] The proposed method uses less labeled data in entity recognition and provides better technical support for tourism recommendation.
Key words： Transfer Learning      BERT      Conditional Random Fields      Scenery Spot Recognition     
Received: 05 August 2019      Published: 15 June 2020
ZTFLH:  TP393  
Corresponding Authors: Sun Lianying     E-mail: sunlychina@163.com
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Ping Zhao
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Cite this article:

Zhao Ping,Sun Lianying,Tu Shuai,Bian Jianling,Wan Ying. Identifying Scenic Spot Entities Based on Improved Knowledge Transfer. Data Analysis and Knowledge Discovery, 2020, 4(5): 118-126.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0907     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I5/118

BBC Entity Recognition Model
Algorithm Structure
项 数量（个）
实体数 74 430
非实体数 457 040
总量 531 470
Data Distribution
字 词性 标注
我 r O
今 t O
天 t O
去 v O
了 ul O
故 n B-SE
宫 n I-SE
Labeling Examples
Data Characteristics
方法 P R F1
CRF 86.67% 87.84% 87.25%
BiLSTM 93.25% 87.98% 90.53%
BiLSTM+CRF 94.97% 92.10% 93.52%
BBC 96.79% 96.85% 96.74%
Model Layer Verification
i P R F1
0.40 84.64% 64.01% 72.89%
0.45 87.26% 69.28% 77.24%
0.50 90.93% 53.85% 67.64%
0.55 93.14% 55.42% 69.49%
0.60 91.41% 55.74% 69.25%
Experimental Results with Different Values of i
simsen P R F1
0.40 89.01% 56.07% 68.80%
0.45 91.30% 57.79% 70.78%
0.50 92.05% 58.16% 71.28%
0.55 91.03% 55.99% 69.33%
0.60 90.81% 56.50% 69.66%
Experimental Results with Different Values of simsen
SEA P R F1
0.40 87.26% 79.28% 83.07%
0.45 90.93% 83.85% 87.24%
0.50 93.14% 85.42% 89.11%
0.55 91.41% 85.74% 88.48%
0.60 90.81% 83.50% 87.00%
Experimental Results with Different Values of SEA
μ P R F1
1/5 93.14% 85.42% 89.11%
1/4 95.06% 82.12% 88.12%
1/3 97.91% 89.15% 93.30%
1/2 98.41% 88.09% 92.97%
Experimental Results with Different Values of μ
模型 μ P R F1
BBC 1 96.79% 96.85% 96.74%
1/5 93.14% 85.42% 89.11%
AttTrBBC 1/4 95.06% 82.12% 88.12%
1/3 97.91% 89.15% 93.30%
1/2 98.41% 88.09% 92.97%
Comparison BetweenAll Annotations and a Few Annotations
方法 P R F1
HMM[11] 85.49% 90.14% 87.75%
CRF[10] 83.40% 95.70% 89.10%
CNN[12] 95.03% 92.80% 93.90%
AttTrBBC 98.41% 88.09% 92.97%
Comparative Analysis of Four Methods
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