{"id":106324,"date":"2025-03-25T14:59:44","date_gmt":"2025-03-25T09:29:44","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/what-is-lemmatization\/"},"modified":"2025-03-25T12:00:41","modified_gmt":"2025-03-25T06:30:41","slug":"what-is-lemmatization","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/what-is-lemmatization\/","title":{"rendered":"What is Lemmatization in NLP?"},"content":{"rendered":"\n<p>Have you ever wondered how search engines understand your queries, even when you use different word forms? Or how chatbots comprehend and respond accurately, despite variations in language?<\/p>\n\n\n\n<p>The answer lies in <a href=\"https:\/\/www.mygreatlearning.com\/blog\/natural-language-processing-tutorial\/\"><strong>Natural Language Processing<\/strong><\/a><strong> (NLP)<\/strong>, a fascinating branch of <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-artificial-intelligence\/\">artificial intelligence<\/a> that enables machines to understand and process human language.<\/p>\n\n\n\n<p>One of the key techniques in NLP is <strong>lemmatization<\/strong>, which refines text processing by reducing words to their base or dictionary form. Unlike simple word truncation, lemmatization takes <strong>context and meaning<\/strong> into account, ensuring more accurate language interpretation.<\/p>\n\n\n\n<p>Whether it\u2019s enhancing search results, improving chatbot interactions, or aiding text analysis, lemmatization plays a crucial role in multiple applications.<\/p>\n\n\n\n<p>In this article, we\u2019ll explore what lemmatization is, how it differs from stemming, its importance in NLP, and how you can implement it in <a href=\"https:\/\/www.mygreatlearning.com\/blog\/python-tutorial-for-beginners-a-complete-guide\/\">Python<\/a>. Let\u2019s dive in!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-is-lemmatization\"><strong>What is Lemmatization?<\/strong><\/h2>\n\n\n\n<p>Lemmatization is the process of converting a word to its <strong>base form (lemma)<\/strong> while considering its context and meaning. Unlike <strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-stemming-in-nlp\/\">stemming<\/a><\/strong>, which simply removes suffixes to generate root words, lemmatization ensures that the transformed word is a valid dictionary entry. This makes lemmatization more accurate for text processing.<\/p>\n\n\n\n<p>For example:<\/p>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmanization-example.png\"><img decoding=\"async\" width=\"920\" height=\"573\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmanization-example.png\" alt=\"Lemmatization Example\" class=\"wp-image-106327\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmanization-example.png 920w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmanization-example-300x187.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmanization-example-768x478.png 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmanization-example-150x93.png 150w\" sizes=\"(max-width: 920px) 100vw, 920px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Running \u2192 Run<\/li>\n\n\n\n<li>Studies \u2192 Study<\/li>\n\n\n\n<li>Better \u2192 Good (Lemmatization considers meaning, unlike stemming)<\/li>\n<\/ul>\n\n\n\n<p class=\"block-course-highlighter\"><strong>Also Read:<\/strong> <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-stemming-in-nlp\/\">What is Stemming in NLP?<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-lemmatization-works\"><strong>How Lemmatization Works<\/strong><\/h2>\n\n\n\n<p>Lemmatization typically involves:<\/p>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-process.png\"><img decoding=\"async\" width=\"842\" height=\"554\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-process.png\" alt=\"Lemmatization Process\" class=\"wp-image-106328\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-process.png 842w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-process-300x197.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-process-768x505.png 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-process-150x99.png 150w\" sizes=\"(max-width: 842px) 100vw, 842px\" \/><\/figure>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/tokenization\/\">Tokenization<\/a><\/strong>: Splitting text into words.\n<ul class=\"wp-block-list\">\n<li>Example: Sentence: <em>\"The cats are playing in the garden.\"<\/em><\/li>\n\n\n\n<li>After tokenization: ['The', 'cats', 'are', 'playing', 'in', 'the', 'garden']<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/pos-tagging\/\">Part-of-Speech (POS) Tagging<\/a><\/strong>: Identifying a word\u2019s role (noun, verb, adjective, etc.).\n<ul class=\"wp-block-list\">\n<li>Example: cats (noun), are (verb), playing (verb), garden (noun)<\/li>\n\n\n\n<li>POS tagging helps distinguish between words with multiple forms, such as \"running\" (verb) vs. \"running\" (adjective, as in \"running water\").<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Applying Lemmatization Rules<\/strong>: Converting words into their base form using a lexical database.\n<ul class=\"wp-block-list\">\n<li>Example:\n<ul class=\"wp-block-list\">\n<li>playing \u2192 play<\/li>\n\n\n\n<li>cats \u2192 cat<\/li>\n\n\n\n<li>better \u2192 good<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Without POS tagging, \"playing\" might not be lemmatized correctly. POS tagging ensures that \"playing\" is correctly transformed into \"play\" as a verb.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"example-1-standard-verb-lemmatization\"><strong>Example 1: Standard Verb Lemmatization<\/strong><\/h3>\n\n\n\n<p>Consider a sentence: <em>\"She was running and had studied all night.\"<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Without lemmatization: ['was', 'running', 'had', 'studied', 'all', 'night']<\/li>\n\n\n\n<li>With lemmatization: ['be', 'run', 'have', 'study', 'all', 'night']<\/li>\n\n\n\n<li>Here, \"was\" is converted to \"be\", \"running\" to \"run\", and \"studied\" to \"study\", ensuring the base forms are recognized.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"example-2-adjective-lemmatization\"><strong>Example 2: Adjective Lemmatization<\/strong><\/h3>\n\n\n\n<p>Consider: <em>\"This is the best solution to a better problem.\"<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Without lemmatization: ['best', 'solution', 'better', 'problem']<\/li>\n\n\n\n<li>With lemmatization: ['good', 'solution', 'good', 'problem']<\/li>\n\n\n\n<li>Here, \"best\" and \"better\" are reduced to their base form \"good\" for accurate meaning representation.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"why-is-lemmatization-important-in-nlp\"><strong>Why is Lemmatization Important in NLP?<\/strong><\/h2>\n\n\n\n<p>Lemmatization plays a key role in improving text normalization and understanding. Its significance includes:<\/p>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-importance.png\"><img decoding=\"async\" width=\"998\" height=\"434\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-importance.png\" alt=\"Importance of Lemmatization\" class=\"wp-image-106329\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-importance.png 998w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-importance-300x130.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-importance-768x334.png 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-importance-150x65.png 150w\" sizes=\"(max-width: 998px) 100vw, 998px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Better Text Representation<\/strong>: Converts different word forms into a single form for efficient processing.<\/li>\n\n\n\n<li><strong>Improved Search Engine Results<\/strong>: Helps search engines match queries with relevant content by recognizing different word variations.<\/li>\n\n\n\n<li><strong>Enhanced NLP Models<\/strong>: Reduces dimensionality in <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-machine-learning\/\">machine learning<\/a> and NLP tasks by grouping words with similar meanings.<\/li>\n<\/ul>\n\n\n\n<p class=\"block-course-highlighter\">Learn how<a href=\"https:\/\/www.mygreatlearning.com\/blog\/text-summarization-in-python\/\"> <strong>Text Summarization in Python<\/strong><\/a> works and explore techniques like extractive and abstractive summarization to condense large texts efficiently.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"lemmatization-vs-stemming\"><strong>Lemmatization vs. Stemming<\/strong><\/h2>\n\n\n\n<p>Both lemmatization and stemming aim to reduce words to their base forms, but they differ in approach and accuracy:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Feature<\/strong><\/td><td><strong>Lemmatization<\/strong><\/td><td><strong>Stemming<\/strong><\/td><\/tr><tr><td>Approach<\/td><td>Uses linguistic knowledge and context<\/td><td>Uses simple truncation rules<\/td><\/tr><tr><td>Accuracy<\/td><td>High (produces dictionary words)<\/td><td>Lower (may create non-existent words)<\/td><\/tr><tr><td>Processing Speed<\/td><td>Slower due to linguistic analysis<\/td><td>Faster but less accurate<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/stemming-vs-lemanization.png\"><img decoding=\"async\" width=\"734\" height=\"377\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/stemming-vs-lemanization.png\" alt=\"Stemming vs Lemmatization, which one to Use?\" class=\"wp-image-106330\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/stemming-vs-lemanization.png 734w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/stemming-vs-lemanization-300x154.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/stemming-vs-lemanization-150x77.png 150w\" sizes=\"(max-width: 734px) 100vw, 734px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"implementing-lemmatization-in-python\"><strong>Implementing Lemmatization in Python<\/strong><\/h2>\n\n\n\n<p>Python provides libraries like <a href=\"https:\/\/www.mygreatlearning.com\/blog\/nltk-tutorial-with-python\/\"><strong>NLTK<\/strong><\/a> and <strong>spaCy<\/strong> for lemmatization.<\/p>\n\n\n\n<p><strong>Using NLTK:<\/strong><\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.corpus import wordnet\nimport nltk\nnltk.download(&#039;wordnet&#039;)\nnltk.download(&#039;omw-1.4&#039;)\n\nlemmatizer = WordNetLemmatizer()\nprint(lemmatizer.lemmatize(&quot;running&quot;, pos=&quot;v&quot;))  # Output: run\n<\/pre><\/div>\n\n\n<p><strong>Using spaCy:<\/strong><\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nimport spacy\nnlp = spacy.load(&quot;en_core_web_sm&quot;)\ndoc = nlp(&quot;running studies better&quot;)\nprint(&#x5B;token.lemma_ for token in doc])  # Output: &#x5B;&#039;run&#039;, &#039;study&#039;, &#039;good&#039;]\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"applications-of-lemmatization\"><strong>Applications of Lemmatization<\/strong><\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-applications.png\"><img decoding=\"async\" width=\"761\" height=\"451\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-applications.png\" alt=\"Applications of Lemmatization\" class=\"wp-image-106331\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-applications.png 761w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-applications-300x178.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/lemmatization-applications-150x89.png 150w\" sizes=\"(max-width: 761px) 100vw, 761px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Chatbots &amp; Virtual Assistants<\/strong>: Understands user inputs better by normalizing words.<\/li>\n\n\n\n<li><strong>Sentiment Analysis<\/strong>: Groups words with similar meanings for better sentiment detection.<\/li>\n\n\n\n<li><strong>Search Engines<\/strong>: Enhances search relevance by treating different word forms as the same entity.<\/li>\n<\/ul>\n\n\n\n<p class=\"block-course-highlighter\"><strong>Suggested:<\/strong> <a href=\"https:\/\/www.mygreatlearning.com\/nlp\/free-courses\">Free NLP Courses<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"challenges-of-lemmatization\"><strong>Challenges of Lemmatization<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Computational Cost<\/strong>: Slower than stemming due to linguistic processing.<\/li>\n\n\n\n<li><strong>POS Tagging Dependency<\/strong>: Requires correct tagging to generate accurate results.<\/li>\n\n\n\n<li><strong>Ambiguity<\/strong>: Some words have multiple valid lemmas based on context.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"future-trends-in-lemmatization\"><strong>Future Trends in Lemmatization<\/strong><\/h2>\n\n\n\n<p>With advancements in AI and NLP , lemmatization is evolving with:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Deep Learning-Based Lemmatization<\/strong>: Using transformer models like <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-bert\/\">BERT<\/a> for context-aware lemmatization.<\/li>\n\n\n\n<li><strong>Multilingual Lemmatization<\/strong>: Supporting multiple languages for global <a href=\"https:\/\/www.mygreatlearning.com\/blog\/trending-natural-language-processing-applications\/\">NLP applications<\/a>.<\/li>\n\n\n\n<li><strong>Integration with <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-llm\/\">Large Language Models (LLMs)<\/a><\/strong>: Enhancing accuracy in conversational AI and text analysis.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"conclusion\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>Lemmatization is an essential NLP technique that refines text processing by reducing words to their dictionary forms. It improves the accuracy of NLP applications, from search engines to chatbots. While it comes with challenges, its future looks promising with AI-driven improvements.<\/p>\n\n\n\n<p>By leveraging lemmatization effectively, businesses and developers can enhance text analysis and build more intelligent NLP solutions.<\/p>\n\n\n\n<p class=\"block-course-highlighter\">Master NLP and lemmatization techniques as part of the <a href=\"https:\/\/www.mygreatlearning.com\/pg-program-artificial-intelligence-course\">PG Program in Artificial Intelligence &amp; Machine Learning<\/a>.<br><br>This program dives deep into AI applications, including Natural Language Processing and Generative AI, helping you build real-world AI solutions. Enroll today and take advantage of expert-led training and hands-on projects.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"frequently-asked-questionsfaqs\"><strong>Frequently Asked Questions(FAQ\u2019s)<\/strong><\/h2>\n\n\n\n<p><strong>What is the difference between lemmatization and tokenization in NLP?<br><\/strong>Tokenization breaks text into individual words or phrases, whereas lemmatization converts words into their base form for meaningful language processing.<\/p>\n\n\n\n<p><strong>How does lemmatization improve text classification in machine learning?<br><\/strong>Lemmatization reduces word variations, helping <a href=\"https:\/\/www.mygreatlearning.com\/blog\/machine-learning-models\/\">machine learning models<\/a> identify patterns and improve classification accuracy by normalizing text input.<\/p>\n\n\n\n<p><strong>Can lemmatization be applied to multiple languages?<br><\/strong>Yes, modern NLP libraries like spaCy and Stanza support multilingual lemmatization, making it useful for diverse linguistic applications.<\/p>\n\n\n\n<p><strong>Which NLP tasks benefit the most from lemmatization?<br><\/strong>Lemmatization enhances search engines, chatbots, sentiment analysis, and text summarization by reducing redundant word forms.<\/p>\n\n\n\n<p><strong>Is lemmatization always better than stemming for NLP applications?<\/strong><strong><br><\/strong>While lemmatization provides more accurate word representations, stemming is faster and may be preferable for tasks that prioritize speed over precision.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Lemmatization in NLP refines text processing by reducing words to their dictionary form, considering context for accurate interpretation. Unlike stemming, it produces valid dictionary words and enhances applications like search engines, chatbots, and sentiment analysis. This article covers its importance, examples, and implementation.<\/p>\n","protected":false},"author":41,"featured_media":106326,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[36798],"content_type":[],"class_list":["post-106324","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-nlp"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>What is Lemmatization in NLP?<\/title>\n<meta name=\"description\" content=\"Lemmatization in NLP converts words to their base form, improving text processing for search engines, chatbots, and sentiment analysis. Learn its significance and implementation.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-lemmatization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is Lemmatization in NLP?\" \/>\n<meta property=\"og:description\" content=\"Lemmatization in NLP converts words to their base form, improving text processing for search engines, chatbots, and sentiment analysis. 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