Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Hu, Kai; Luo, Qing; Qi, Kunlun; Yang, Siluo; Mao, Jin; Fu, Xiaokang; Zheng, Jie; Wu, Huayi; Guo, Ya; Zhu, Qihong

doi:10.1016/j.ipm.2019.02.014

Cited by 63 publications

(21 citation statements)

References 26 publications

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“…According to Professor Gao, landscape architecture is a complex system discipline that focuses on the construction of human outdoor living environment, integrating art, science, and engineering technology, with emphasis on the planning and design of various environments, involving landscape design to the management of natural resources in the national territory [13]. From the perspective of geography, Mr. Hu, a famous human geographer, proposed that a settlement landscape is a complex with a consistent physical geographic basis, a similar degree of utilization and development process, similar morphological structure, and functions, and interconnected and coordinated elements in the settlement area [14]. Settlement landscape is a mosaic of different land units under the constraints of natural environmental conditions and the dual influence of human management activities and management strategies, and the mosaic of different land units into the territorial scope of the settlement, size, shape, and configuration of the mosaic has a large heterogeneity, both economic value, social value, ecological value, and aesthetic value [15].…”

Section: Related Studiesmentioning

confidence: 99%

[Retracted] Extraction of Spatial Distribution Characteristics of Jiangnan Urban Landscape under the Influence of Geomorphology

Wang

Qin

2021

Complexity

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This paper presents an in-depth study and analysis of the spatial distribution of urban landscapes in Jiangnan cities under the influence of geomorphology and extracts the characteristics to explore the construction mechanism from the perspective of spatial functionality based on the analysis of the process and content of landscape construction. The construction of cities and ponds originated from political influence, environmental constraints, and architectural techniques; the landscape pattern of the combined shape and complementary potential originated from the creation of various types of gardens in various dynasties; the complementary planning of landscape, the traffic creation of the plain and water network, and the production factors of agriculture and sanitation influenced the construction of the countryside landscape; the traditional view of feng shui, poetry and painting, the eight scenes of the city and countryside, and other humanistic intentions influenced the overall situation of the town and the landscape. This paper extracts and analyzes the spatial distribution characteristics of Jiangnan city landscape from several perspectives, and the results can clearly show the distribution of landscape features under the influence of geomorphology and can give the best layout suggestions. From the perspective of spatial variability, the main features of traditional landscape construction in Jiangnan are analyzed through three levels: the characteristics of the process of humanized landscapes evolving into natural landscapes, the comparative analysis of similarities and differences in the construction of cities and towns, and the characteristics of the system of regional landscape construction. Finally, the changes in the landscape pattern of the region under the influence of globalization are analyzed through the changing characteristics of landscape architecture.

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Section: Related Studiesmentioning

confidence: 99%

[Retracted] Extraction of Spatial Distribution Characteristics of Jiangnan Urban Landscape under the Influence of Geomorphology

Wang

Qin

2021

Complexity

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“…For example, Xu proposed a manifold learning-based model to explore topic-sentiment associations and their evolution over time in the online news domain that can visualize the hidden sentiment dynamics of topics in a low-dimensional space [23]. Hu applied a deep learning language model, Google Word2Vec, to find whether and how keyword semantics can invoke or affect topic evolution [24]. Yang proposed an ordering-sensitive and semantic-aware dynamic author topic model that monitors the evolution of author interest in timestamped documents [25].…”

Section: Fields Methods Contextsmentioning

confidence: 99%

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

et al. 2021

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Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

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“…Word2vec is an open-source tool provided by Google in 2013. The first relevant paper was written by Mikolov [43]. This method is based on neural networks and is used to display words in Shape Vector view.…”

Section: Word2vecmentioning

confidence: 99%

Automated classification of BI-RADS in textual mammography reports

Boroumandzadeh¹,

Parvinnia²

2021

Turk J Elec Eng & Comp Sci

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The main purpose of this paper is to process key information in medical text records and also classify patients, per different levels of Breast Imaging-Reporting and Data system (BI-RADS). The BI-RADS is a scheme for the standardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reports supported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction from textual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosing keywords from medical text reports. Word2vec and Term Frequency Inverse Document Frequency (TFIDF) techniques are used for extracting features, finally, they're combined with the Hospital Information System (HIS) reports and called With-HIS. The different classifiers like multi-class Support Vector Machine (SVM), Naïve Bayesian (NB), Extreme Gradient Boosting (XGBoost), and Multi-Level Fuzzy Min-Max Neural Network (MLF) are used so as to compare the accuracy of With-HIS and Without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach (Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within the proposed method with MLF classifier is 0.89% but without-HIS is 0.85%.

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Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Cited by 63 publications

References 26 publications

[Retracted] Extraction of Spatial Distribution Characteristics of Jiangnan Urban Landscape under the Influence of Geomorphology

[Retracted] Extraction of Spatial Distribution Characteristics of Jiangnan Urban Landscape under the Influence of Geomorphology

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

Automated classification of BI-RADS in textual mammography reports

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