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Each text comes with different words and requires specific language skills. Choosing the right words depending on the context and the purpose of the content, is more complicated. It helps a machine to better understand human language through a distributed representation of the text in an n-dimensional space. The technique is highly used in NLP challenges \u2014 one of them being to understand the context of words. Yes, words make up text data, however, words and phrases have different meanings depending on the context of a sentence. As humans, from birth, we learn and adapt to understand the context.<\/p>\n<\/p>\n
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Things are getting smarter with NLP<\/a> ( Natural Language Processing ) . Yesterday I met my friend who is using chatbot for mobile recharge . If you look at whats going on IT sectors ,you will see ,\u201dSuddenly the IT Industry is taking a sharp turn where machine are more human like \u201c.<\/p>\n<\/p>\n Srihari [129] explains the different generative models as one with a resemblance that is used to spot an unknown speaker\u2019s language and would bid the deep knowledge of numerous languages to perform the match. Discriminative methods rely on a less knowledge-intensive approach and using distinction between languages. Whereas<\/a> generative models can become troublesome when many features are used and discriminative models allow use of more features [38]. Few of the examples of discriminative methods are Logistic regression and conditional random fields (CRFs), generative methods are Naive Bayes classifiers and hidden Markov models (HMMs). The type of algorithm data scientists choose depends on the nature of the data. Many of the algorithms and techniques aren’t limited to just one of the primary ML types listed here.<\/p>\n<\/p>\n Some of the tasks such as automatic summarization, co-reference analysis etc. act as subtasks that are used in solving larger tasks. Nowadays NLP is in the talks because of various applications and recent developments although in the late 1940s the term wasn\u2019t even in existence. So, it will be interesting to know about the history of NLP, the progress so far has been made and some of the ongoing projects by making use of NLP.<\/p>\n<\/p>\n Training the output-symbol chain data, reckon the state-switch\/output probabilities that fit this data best. There is a system called MITA (Metlife\u2019s Intelligent Text Analyzer) (Glasgow et al. (1998) [48]) that extracts information from life insurance applications. Ahonen et al. (1998) [1] suggested a mainstream framework for text mining that uses pragmatic and discourse level analyses of text. We first give insights on some of the mentioned tools and relevant work done before moving to the broad applications of NLP. It then gives you recommendations on correcting the word and improving the grammar.<\/p>\n<\/p>\nData labeling<\/h2>\n<\/p>\n
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3 Information Extraction and Mapping \u2013<\/h2>\n<\/p>\n