Chinese Text Emotional Analysis Based on Bi-LSTM Model Fusing Emotional Features

2021 ◽  
pp. 225-241
Author(s):  
Hao Li ◽  
Jian-cong Fan
Keyword(s):  
2016 ◽  
Author(s):  
Teng Zhang ◽  
Zhipeng Chen ◽  
Ji Wu ◽  
Sam Lai ◽  
Wenhui Lei ◽  
...  

Author(s):  
Xiujie Ma ◽  
George Jennings

In a globalized, media-driven society, people are being exposed to different cultural and philosophical ideas. In Europe, the School of Internal Arts (pseudonym) follows key principles of the ancient Chinese text The Yijinjing (The Muscle-Tendon Change Classic) “Skeleton up, flesh down”, in its online and offline pedagogy. This article draws on an ongoing ethnographic, netnographic and cross-cultural investigation of the transmission of knowledge in this atypical association that combines Taijiquan with a range of practices such as Qigong, body loosening exercises and meditation. Exploring the ideal body cultivated by the students, we describe and illustrate key (and often overlooked) body areas—namely the spine, scapula, Kua and feet, which are continually worked on in the School of Internal Arts’ exercise-based pedagogy. We argue that Neigong and Taijiquan, rather than being forms of physical education, are vehicles for adult physical re-education. This re-education offers space in which mind-body tension built over the life course are systematically released through specific forms of attentive, meditative exercise to lay the foundations for a strong, powerful body for martial artistry and health.


Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 275
Author(s):  
Igor A. Bessmertny ◽  
Xiaoxi Huang ◽  
Aleksei V. Platonov ◽  
Chuqiao Yu ◽  
Julia A. Koroleva

Search engines are able to find documents containing patterns from a query. This approach can be used for alphabetic languages such as English. However, Chinese is highly dependent on context. The significant problem of Chinese text processing is the missing blanks between words, so it is necessary to segment the text to words before any other action. Algorithms for Chinese text segmentation should consider context; that is, the word segmentation process depends on other ideograms. As the existing segmentation algorithms are imperfect, we have considered an approach to build the context from all possible n-grams surrounding the query words. This paper proposes a quantum-inspired approach to rank Chinese text documents by their relevancy to the query. Particularly, this approach uses Bell’s test, which measures the quantum entanglement of two words within the context. The contexts of words are built using the hyperspace analogue to language (HAL) algorithm. Experiments fulfilled in three domains demonstrated that the proposed approach provides acceptable results.


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