As H1N1 influenza rages across the world from the very beginning of mid 2009,lots of news reports focus on it.This essay mainly studies the stylistics features of News English by reading news reports on H1N1.The study...As H1N1 influenza rages across the world from the very beginning of mid 2009,lots of news reports focus on it.This essay mainly studies the stylistics features of News English by reading news reports on H1N1.The study tries to find lexical features,sentence features and grammatical features of News English.Through the study we find that News English has its own stylistics features in words,sentences and grammar.展开更多
Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is...Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity.In social behavior,sentiment can be thought of as a latent variable.Measuring and comprehending this behavior could help us to better understand the social issues.Because sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world scenario.Textual sentiment analysis is done in sentence,document level and feature levels.This work introduces a new Information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant ones.Extensive textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm.The analysis is done based on the datasets from Cornell and Kaggle repositories.When compared to existing baseline classifiers,the suggested Information Gain based classifier resulted in an increased accuracy of 96%for document,97.4%for sentence and 98.5%for feature levels respectively.Also,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 datasets.Experimental results for these high dimensional datasets give increased accuracy of 95%,96%and 98%for the proposed Information Gain based classifier for document,sentence and feature levels respectively compared to existing baseline classifiers.展开更多
Because of the ambiguity and dynamic nature of natural language,the research of named entity recognition is very challenging.As an international language,English plays an important role in the fields of science and te...Because of the ambiguity and dynamic nature of natural language,the research of named entity recognition is very challenging.As an international language,English plays an important role in the fields of science and technology,finance and business.Therefore,the early named entity recognition technology is mainly based on English,which is often used to identify the names of people,places and organizations in the text.International conferences in the field of natural language processing,such as CoNLL,MUC,and ACE,have identified named entity recognition as a specific evaluation task,and the relevant research uses evaluation corpus from English-language media organizations such as the Wall Street Journal,the New York Times,and Wikipedia.The research of named entity recognition on relevant data has achieved good results.Aiming at the sparse distribution of entities in text,a model combining local and global features is proposed.The model takes a single English character as input,and uses the local feature layer composed of local attention and convolution to process the text pieceby way of sliding window to construct the corresponding local features.In addition,the self-attention mechanism is used to generate the global features of the text to improve the recognition effect of the model on long sentences.Experiments on three data sets,Resume,MSRA and Weibo,show that the proposed method can effectively improve the model’s recognition of English named entities.展开更多
文摘As H1N1 influenza rages across the world from the very beginning of mid 2009,lots of news reports focus on it.This essay mainly studies the stylistics features of News English by reading news reports on H1N1.The study tries to find lexical features,sentence features and grammatical features of News English.Through the study we find that News English has its own stylistics features in words,sentences and grammar.
文摘Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity.In social behavior,sentiment can be thought of as a latent variable.Measuring and comprehending this behavior could help us to better understand the social issues.Because sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world scenario.Textual sentiment analysis is done in sentence,document level and feature levels.This work introduces a new Information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant ones.Extensive textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm.The analysis is done based on the datasets from Cornell and Kaggle repositories.When compared to existing baseline classifiers,the suggested Information Gain based classifier resulted in an increased accuracy of 96%for document,97.4%for sentence and 98.5%for feature levels respectively.Also,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 datasets.Experimental results for these high dimensional datasets give increased accuracy of 95%,96%and 98%for the proposed Information Gain based classifier for document,sentence and feature levels respectively compared to existing baseline classifiers.
基金Reform and Practice of Practical Teaching System for Applied Translation Undergraduate Majors from the Perspective of Technology Hard Trend of Henan Province Education Reform Project in 2024(Project number:2024SJGLX0581)Teaching Reform Project of Zhengzhou University of Science and Technology in 2024,”Innovative Research on Practical Teaching of Digital-Intelligence Technology Enabling Production-Teaching Integration”(Project number:2024JGZD11).
文摘Because of the ambiguity and dynamic nature of natural language,the research of named entity recognition is very challenging.As an international language,English plays an important role in the fields of science and technology,finance and business.Therefore,the early named entity recognition technology is mainly based on English,which is often used to identify the names of people,places and organizations in the text.International conferences in the field of natural language processing,such as CoNLL,MUC,and ACE,have identified named entity recognition as a specific evaluation task,and the relevant research uses evaluation corpus from English-language media organizations such as the Wall Street Journal,the New York Times,and Wikipedia.The research of named entity recognition on relevant data has achieved good results.Aiming at the sparse distribution of entities in text,a model combining local and global features is proposed.The model takes a single English character as input,and uses the local feature layer composed of local attention and convolution to process the text pieceby way of sliding window to construct the corresponding local features.In addition,the self-attention mechanism is used to generate the global features of the text to improve the recognition effect of the model on long sentences.Experiments on three data sets,Resume,MSRA and Weibo,show that the proposed method can effectively improve the model’s recognition of English named entities.
