scholarly journals HiTR: Hierarchical Topic Model Re-Estimation for Measuring Topical Diversity of Documents

2019 ◽  
Vol 31 (11) ◽  
pp. 2124-2137
Author(s):  
Hosein Azarbonyad ◽  
Mostafa Dehghani ◽  
Tom Kenter ◽  
Maarten Marx ◽  
Jaap Kamps ◽  
...  
Keyword(s):  
Topic Model ◽  
Topical Diversity

A Study on Bestseller Short Text Semantics Analysis Using Topic Model

2018 ◽  
Vol 15 ◽  
pp. 101-112
Author(s):  
So-Hyun Park ◽  
Ae-Rin Song ◽  
Young-Ho Park ◽  
Sun-Young Ihm
Keyword(s):  
Topic Model ◽  
Short Text

Twin labeled LDA: a supervised topic model for document classification

2020 ◽  
Vol 50 (12) ◽  
pp. 4602-4615
Author(s):  
Wei Wang ◽  
Bing Guo ◽  
Yan Shen ◽  
Han Yang ◽  
Yaosen Chen ◽  
...  
Keyword(s):  
Topic Model ◽  
Document Classification

Research Topics and Trends in the Maritime Transport: a Structural Topic Model

2020 ◽  
Author(s):  
Xiwen Bai ◽  
Xiunian Zhang ◽  
Kevin X. Li ◽  
Yaoming Zhou ◽  
Kum Fai Yuen
Keyword(s):  
Topic Model ◽  
Maritime Transport ◽  
Research Topics ◽  
Structural Topic Model

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

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