For English sentences with a large amount of feature data and complex pronunciation changes contrast to words, there are more problems existing in Hidden Markov Model (HMM), such as the computational complexity of the...For English sentences with a large amount of feature data and complex pronunciation changes contrast to words, there are more problems existing in Hidden Markov Model (HMM), such as the computational complexity of the Viterbi algorithm and mixed Gaussian distribution probability. This article explores the segment-mean algorithm for dimensionality reduction of speech feature parameters, the clustering cross-grouping algorithm and the HMM grouping algorithm, which are proposed for the implementation of the speaker-independent English sentence recognition system based on HMM and clustering. The experimental result shows that, compared with the single HMM, it improves not only the recognition rate but also the recognition speed of the system.展开更多
Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,...Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.展开更多
The contributions of scientific researchers include personal influence and talent training achievements. In this paper, using 9964 high-quality coauthor scientific papers in English teaching research from China citati...The contributions of scientific researchers include personal influence and talent training achievements. In this paper, using 9964 high-quality coauthor scientific papers in English teaching research from China citation database from 1997 to 2016, a weighted coauthor network with variety factors is constructed. A model was proposed to calculate the author’s contribution to the research team by combining personal and network characteristics. The results reveal a variety of characteristics of the co-author networks in English teaching research field, including statistical properties, community features, and authors’ contribution to teams in this discipline.展开更多
文摘For English sentences with a large amount of feature data and complex pronunciation changes contrast to words, there are more problems existing in Hidden Markov Model (HMM), such as the computational complexity of the Viterbi algorithm and mixed Gaussian distribution probability. This article explores the segment-mean algorithm for dimensionality reduction of speech feature parameters, the clustering cross-grouping algorithm and the HMM grouping algorithm, which are proposed for the implementation of the speaker-independent English sentence recognition system based on HMM and clustering. The experimental result shows that, compared with the single HMM, it improves not only the recognition rate but also the recognition speed of the system.
基金This work is partially supported by the National Natural Science Foundation of China under Grant Nos.61876205 and 61877013the Ministry of Education of Humanities and Social Science project under Grant Nos.19YJAZH128 and 20YJAZH118+1 种基金the Science and Technology Plan Project of Guangzhou under Grant No.201804010433the Bidding Project of Laboratory of Language Engineering and Computing under Grant No.LEC2017ZBKT001.
文摘Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.
基金the National Natural Science Foundation of China (No. 61402119).
文摘The contributions of scientific researchers include personal influence and talent training achievements. In this paper, using 9964 high-quality coauthor scientific papers in English teaching research from China citation database from 1997 to 2016, a weighted coauthor network with variety factors is constructed. A model was proposed to calculate the author’s contribution to the research team by combining personal and network characteristics. The results reveal a variety of characteristics of the co-author networks in English teaching research field, including statistical properties, community features, and authors’ contribution to teams in this discipline.