{"id":24902,"date":"2018-08-17T04:35:17","date_gmt":"2018-08-17T04:35:17","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=24902"},"modified":"2026-04-29T11:22:03","modified_gmt":"2026-04-29T05:52:03","slug":"nlp-tutorial-natural-language-processing","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/nlp-tutorial-natural-language-processing\/","title":{"rendered":"NLP Tutorial AI with Python | Natural Language Processing"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Today, in our journey of <strong>Artificial Intelligence <\/strong>with<strong> Python<\/strong>, we will discuss NLP Tutorial, we will discuss the rudiments of Natural Language Processing. We will start our NLP tutorial with NLP definition and a brief introduction. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Then, we will move towards components, libraries, and benefits of natural processing language. Also, we will discuss <strong>Natural Language Processing<\/strong> Applications, NLP examples, and tools.\u00a0<\/span><\/p>\n<p>So, let&#8217;s start NLP Tutorial.<\/p>\n<h3><strong>Introduction to Natural Language Processing<\/strong><\/h3>\n<p>Natural Language Processing (NLP) is a part of AI that helps machines understand human language. Python is very useful in NLP because it has libraries like NLTK, spaCy, and TextBlob. These tools allow your program to read, analyze, and understand text and speech. With Python, you can build applications like chatbots, translators, and search engines that talk like humans.<\/p>\n<p><span style=\"font-weight: 400\">This faces some challenges like speech recognition, natural language understanding, and natural language generation.<\/span><\/p>\n<p>Let&#8217;s have a look at the Python AI Tutorial<\/p>\n<p><span style=\"font-weight: 400\">Well, NLP is all about developing applications and services that can understand human languages.<\/span><\/p>\n<h3><strong>What is NLP?<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">In layman terms, NLP is a way for computers to analyze human language and derive useful meaning from it. It lets you organize and structure knowledge to let you perform the following tasks-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Automatic Summarization<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Translation<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Named Entity Recognition<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Relationship Extraction<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Sentiment Analysis<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Speech Recognition<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Topic Segmentation<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">NLP analyzes text and allows machines to understand how we speak. It considers the hierarchical structure of language and performs tasks like correcting the grammar, converting speech to text, and translating between languages. In computer science, it is a hard problem.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cWhat do words mean, how do they link together, and what meaning do they make?\u201d<\/span><br \/>\n<span style=\"font-weight: 400\">The greatest challenge to NLP is to accurately judge the intention of words, keeping in mind the ambiguity of the language.<\/span><\/p>\n<h3><strong>Components of NLP<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">While talking of NLP in this Tutorial, we come across two main Components of NLP-<\/span><\/p>\n<div id=\"attachment_24957\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24957\" class=\"wp-image-24957 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01.jpg\" alt=\"NLP Tutorial\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Components-of-NLP-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-24957\" class=\"wp-caption-text\">NLP Tutorial &#8211; Components of NLP<\/p><\/div>\n<h4><strong>a. Natural Language Understanding (NLU)<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Natural Language Understanding revolves around machine reading comprehension. This is an AI-hard problem. An NLU system needs the following components-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Lexicon, Parser, and Grammar rules.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Semantic theory- to guide comprehension.<\/span><\/li>\n<\/ul>\n<h4><strong>b. Natural Language Generation (NLG)<\/strong><\/h4>\n<p><span style=\"font-weight: 400\"><em>NLG<\/em> is concerned with generating natural language. It uses a machine representation system like a knowledge base or a logical form. You can think of it as a translator between data and natural language representation; this is the opposite or NLU. This involves three tasks-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Text Planning-<\/strong> To extract relevant content from the knowledge base.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Sentence Planning-<\/strong> To choose appropriate words, form meaningful phrases, and set sentence tone.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Text Realization-<\/strong> To map the sentence plan into sentence structure.<\/span><\/li>\n<\/ul>\n<h3><strong>Benefits of NLP in Python<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Among the numerous benefits of NLP, here we list out a few-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">To improve the efficiency of the documentation process.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">NLP is used to improve the accuracy of the documentation process.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">To identify pertinent information from large databases.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Together with <strong>Machine Learning<\/strong>, we don\u2019t need to hand-code large sets of rules.<\/span><\/p>\n<h3><strong>Libraries for NLP<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Many open-source libraries let us work with Natural Language Programming. Some of those are-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Natural Language ToolKit (NLTK)-<\/strong> Written in Python; allows modules for processing text, classifying, tokenizing, stemming, parsing, tagging, and more.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Apache OpenNLP-<\/strong> Machine Learning toolkit; allows for tokenizers, sentence segmentation, part-of-speech tagging, chunking, parsing, named entity extraction, and more.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Stanford NLP Suite-<\/strong> Tools for part-of-speech tagging, named entity recognizer, sentiment analysis, conference resolution system, and more.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Gate NLP Library.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>MALLET-<\/strong> <strong>Java package<\/strong> for latent dirichlet\u00a0allocation, clustering, topic modeling, information extraction, document classification, and more.<\/span><\/li>\n<\/ul>\n<h3><strong>Glossary in NLP<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Talking of NLP, we talk:<\/span><\/p>\n<div id=\"attachment_24959\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24959\" class=\"wp-image-24959 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01.jpg\" alt=\"NLP Tutorial\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Glossary-in-NLP-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-24959\" class=\"wp-caption-text\">NLP Tutorial &#8211; Glossary in NLP<\/p><\/div>\n<p><strong>a. Phonology<\/strong><br \/>\n<span style=\"font-weight: 400\">Study of organizing sound systematically.<\/span><\/p>\n<p><strong>b. Morphology<\/strong><br \/>\n<span style=\"font-weight: 400\">Study of constructing words from primitive meaningful units.<\/span><\/p>\n<p><strong>c. Morpheme<\/strong><br \/>\n<span style=\"font-weight: 400\">Primitive unit of meaning in a language.<\/span><\/p>\n<p><strong>d. Syntax<\/strong><br \/>\n<span style=\"font-weight: 400\">Arranging words to form a sentence; determining the structural role of words in sentences and phrases.<\/span><\/p>\n<p><strong>e. Semantics<\/strong><br \/>\n<span style=\"font-weight: 400\">Studying the meanings of words and combining them to make meaningful phrases and sentences.<\/span><\/p>\n<p><strong>f. Pragmatics<\/strong><br \/>\n<span style=\"font-weight: 400\">Using and understanding sentences in various situations; determining how this affects sentence interpretation.<\/span><\/p>\n<p><strong>g. Discourse<\/strong><br \/>\n<span style=\"font-weight: 400\">Understanding how a sentence can affect the next.<\/span><\/p>\n<p><strong>h. World Knowledge<\/strong><br \/>\n<span style=\"font-weight: 400\">General knowledge about the world.<\/span><\/p>\n<h3><strong>Tasks in NLP<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">With Natural Language Processing, we carry out five different tasks-<\/span><\/p>\n<div id=\"attachment_24960\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-24960\" class=\"wp-image-24960 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01.jpg\" alt=\"NLP Tutorial\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Tasks-in-NLP-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-24960\" class=\"wp-caption-text\">NLP Tutorial &#8211; Tasks in NLP<\/p><\/div>\n<h4><strong>a. Lexical Analysis<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Lexical analysis deals with identifying and analyzing word structure. We divide the whole chunk of text into paragraphs, sentences, and words.<\/span><\/p>\n<h4><strong>b. Syntactic Analysis<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Also called parsing, it involves analyzing words in sentences for grammar and rearranging them to determine how they relate to each other. It rejects sentences like \u201cThe apple eats the girl\u201d.<\/span><\/p>\n<h4><strong>c. Semantic Analysis<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">This deals with extracting the dictionary meanings from text. It also maps syntactic structures and objects in the task domain to check for meaningfulness. It rejects statements like \u201ctall stub\u201d.<\/span><\/p>\n<h4><strong>d. Discourse Integration<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">It analyzes the previous sentence to guess the meaning of the current sentence and the one after it.<\/span><\/p>\n<h4><strong>e. Pragmatic Analysis<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">This reinterprets the statement to ensure it correctly determines what the statement means. It tries to retrieve aspects of the language that require knowledge of the real world.<\/span><\/p>\n<h3><strong>NLP in Python Applications<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">With NLP, we can do the following-<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Summarizing blocks of text.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Creating chatbots.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Machine translation.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Fighting spam.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Extracting information.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Automatically generating keyword tags.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Identifying types of entities extracted.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Identifying the sentiment of a string with sentiment analysis.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Reducing words to their roots.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Summarizing.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Question-answering.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Customer service.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Market analysis.<\/span><\/li>\n<\/ul>\n<p>So, this was all in the NLP Tutorial. Hope you like our explanation of Natural Processing Language.<\/p>\n<h3><strong>Conclusion<\/strong><\/h3>\n<p>Text is everywhere, but NLP gives you the &#8216;superpower&#8217; to analyze thousands of documents in just seconds. By learning these basics, you&#8217;re teaching your computers to build a bridge between humans and the machine code.<\/p>\n<p><span style=\"font-weight: 400\">And with this, we conclude our introduction to Natural Language Processing with Python. In this Natural Language Processing Tutorial, we discussed NLP Definition, AI natural language processing, and examples of NLP. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Moreover, we talked about its fundamentals, components, benefits, libraries, terminologies, tasks, and applications. Next, we will demonstrate the<strong> use of<\/strong> <strong>NLTK<\/strong> to implement NLP with Python. Still, if any doubt regarding the NLP Tutorial, ask in the comments tab.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today, in our journey of Artificial Intelligence with Python, we will discuss NLP Tutorial, we will discuss the rudiments of Natural Language Processing. We will start our NLP tutorial with NLP definition and a&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":24969,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,46],"tags":[409,1780,2816,9009,9063,9064,9066,9068,9071,9072,9073,9074,9076,15829],"class_list":["post-24902","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-python","tag-ai-natural-language-processing","tag-benefits-of-nlp","tag-components-of-nlp","tag-natural-language-processing","tag-nlp","tag-nlp-applications","tag-nlp-examples","tag-nlp-in-artificial-intelligence","tag-nlp-natural-language-processing","tag-nlp-python","tag-nlp-tasks","tag-nlp-technology","tag-nlp-with-python","tag-what-is-nlp"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>NLP Tutorial AI with Python | Natural Language Processing - 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