{"id":1589,"date":"2026-10-22T06:23:05","date_gmt":"2026-10-22T06:23:05","guid":{"rendered":"https:\/\/www.interviewbit.com\/varsity\/blog\/?p=1589"},"modified":"2026-10-22T07:29:37","modified_gmt":"2026-10-22T07:29:37","slug":"lemmatization-in-nlp","status":"publish","type":"post","link":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/","title":{"rendered":"Lemmatization in NLP: Meaning, Examples &#038; Stemming vs Lemmatization"},"content":{"rendered":"\n<div class=\"wp-block-group vlf-insights is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"vlf-insights-title wp-block-paragraph\">Key Insights<\/p>\n\n\n\n<ul class=\"wp-block-list vlf-insights-list\">\n<li>Lemmatization in NLP converts inflected words into their dictionary base forms, known as lemmas, using lexical resources, linguistic rules and, where supported, grammatical context.<\/li>\n\n\n\n<li>Unlike stemming, which typically reduces words through suffix-based rules, lemmatization aims to produce meaningful dictionary forms. The appropriate technique depends on the task and the level of linguistic precision required.<\/li>\n\n\n\n<li>Morphology and part-of-speech (POS) tagging help lemmatizers identify the correct base form, particularly when a word&#8217;s grammatical role affects its meaning.<\/li>\n\n\n\n<li>Python libraries such as NLTK and spaCy offer practical lemmatization tools, but their outputs depend on POS information, lexical resources and configuration.<\/li>\n\n\n\n<li>Lemmatization can support text classification, information retrieval and linguistic analysis, but it does not automatically improve every NLP pipeline.<\/li>\n\n\n\n<li>Modern language models often use subword tokenisation and may not require a separate lemmatization step. Preprocessing choices should be evaluated against the specific task.<\/li>\n<\/ul>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization is a natural language processing (NLP) technique that converts a word into its dictionary base form, called a lemma. For example, <em>running<\/em> can become <em>run<\/em>. By grouping related word forms, lemmatization in NLP can help systems analyse text consistently.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine you are building a search system that needs to recognise <em>connect<\/em>, <em>connecting<\/em>, and <em>connected<\/em> as related forms of the same word. Should the system treat them as three separate terms, or bring them together?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is one of the problems lemmatization helps address. However, lemmatization differs from stemming, which typically removes word endings using simpler rules. This article explains what lemmatization means, how morphology and part-of-speech (POS) tagging influence its results, and how to implement it in Python using NLTK and spaCy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What is Lemmatization in NLP?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization maps an inflected word to its canonical dictionary form using vocabulary resources, linguistic rules and, depending on the implementation, grammatical information.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lemma meaning with examples<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A <strong>lemma<\/strong> is the dictionary form used to represent a word and its grammatical variants. For example:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Cars \u2192 car<\/li>\n\n\n\n<li>Running \u2192 run<\/li>\n\n\n\n<li>Better \u2192 good, when used as a comparative adjective<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These lemmatization examples show why the process involves more than removing suffixes. The word <em>better<\/em> cannot reliably become <em>good<\/em> through simple suffix stripping; a lexical resource must recognise the relationship.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here\u2019s something to notice: if you remove the ending from <em>better<\/em>, you will not arrive at <em>good<\/em>. Yet <em>good<\/em> is the appropriate lemma when <em>better<\/em> is used as a comparative adjective. This is why lemmatization needs linguistic knowledge, not just a rule for cutting off suffixes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why Lemmatization matters?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization can reduce vocabulary variation by grouping forms such as <em>walk<\/em>, <em>walks<\/em> and <em>walked<\/em>. This may help text classification, information retrieval and text analysis by making related forms easier to match.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, it is not necessary for every NLP pipeline. Whether it helps depends on the task, model and preprocessing strategy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Finding a lemma requires more than shortening a word. The next section explains the linguistic information and resources that support this process.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1944\" height=\"809\" src=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-part-of-speech-tag.png\" alt=\"Lemmatization workflow showing input text, part-of-speech tagging, lemma identification, and the final normalized dictionary form.\" class=\"wp-image-1591\" srcset=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-part-of-speech-tag.png 1944w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-part-of-speech-tag-300x125.png 300w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-part-of-speech-tag-1024x426.png 1024w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-part-of-speech-tag-768x320.png 768w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-part-of-speech-tag-1536x639.png 1536w\" sizes=\"auto, (max-width: 1944px) 100vw, 1944px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">How does Lemmatization Work?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A lemmatization algorithm uses linguistic rules, vocabulary resources and sometimes grammatical information to identify a suitable base form. The exact process varies across tools and languages.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1774\" height=\"887\" src=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-tokenization-part-.png\" alt=\"Lemmatization workflow showing input text, tokenization, part-of-speech tagging, lexical lookup, and conversion of words such as \u201cchildren\u201d to \u201cchild\u201d and \u201crunning\u201d to \u201crun.\u201d\" class=\"wp-image-1592\" srcset=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-tokenization-part-.png 1774w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-tokenization-part--300x150.png 300w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-tokenization-part--1024x512.png 1024w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-tokenization-part--768x384.png 768w, https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/lemmatization-workflow-showing-input-text-tokenization-part--1536x768.png 1536w\" sizes=\"auto, (max-width: 1774px) 100vw, 1774px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Morphology and word forms<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Morphology in NLP examines how words change form and how they are constructed. Inflection changes a word to express grammatical information without creating a separate dictionary entry. For example, <em>walks<\/em>, <em>walked<\/em> and <em>walking<\/em> can share the lemma <em>walk<\/em>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Word formation can create a different word with a related meaning. For instance, <em>teach<\/em> and <em>teacher<\/em> are related, but they do not share the same lemma. A lemmatizer must distinguish these relationships rather than treating every shared word fragment as equivalent.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The role of part-of-speech tagging<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">POS tagging in NLP identifies a word&#8217;s grammatical role, such as noun, verb or adjective. This matters because the same surface form can have different lemmas depending on how it is used.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Consider <em>meeting<\/em>. As a noun, it may remain <em>meeting<\/em>. As a verb, it can map to <em>meet<\/em>. A lemmatizer that uses grammatical context can make a more appropriate choice.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Quick check:<\/strong> What should <em>meeting<\/em> become?<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>In \u201cThe meeting starts at noon,\u201d it is a noun, so it may remain <em>meeting<\/em>.<\/li>\n\n\n\n<li>In \u201cWe are meeting tomorrow,\u201d it functions as a verb, so it can map to <em>meet<\/em>.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The word has not changed its spelling, but its grammatical role has changed. That is why context matters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Dictionaries, lexical resources, and rules<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Dictionary-based lemmatization uses stored vocabulary information to identify base forms. Resources such as WordNet contain lexical relationships and irregular forms that simple suffix rules may miss.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Rule-based systems apply morphological patterns, often alongside POS tags. Lookup-based systems consult stored mappings. Some tools combine these approaches, and their results depend on the language resources and configuration available.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These mechanisms explain why lemmatization can produce meaningful dictionary forms, while stemming follows a different, usually simpler process.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Stemming vs Lemmatization: What is the Difference?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Stemming typically strips or transforms word endings using heuristic rules. Lemmatization aims to return a recognised dictionary form using lexical and, where supported, grammatical information.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Aspect<\/th><th>Stemming<\/th><th>Lemmatization<\/th><\/tr><\/thead><tbody><tr><td>Method<\/td><td>Applies word-reduction rules<\/td><td>Uses lexical resources, rules and sometimes POS<\/td><\/tr><tr><td>Output<\/td><td>May not be a valid word<\/td><td>A recognised lemma where supported<\/td><\/tr><tr><td>Context<\/td><td>Usually ignores grammatical role<\/td><td>May use POS and morphology<\/td><\/tr><tr><td>Speed<\/td><td>Generally faster<\/td><td>Often more computationally involved<\/td><\/tr><tr><td>Example<\/td><td><em>studies<\/em> \u2192 <em>studi<\/em> with Porter stemming<\/td><td><em>studies<\/em> \u2192 <em>study<\/em><\/td><\/tr><tr><td>Typical use<\/td><td>Lightweight indexing and retrieval<\/td><td>Tasks where meaningful base forms matter<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Examples of stemming and lemmatization<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Word<\/th><th>Porter stemming example<\/th><th>Lemmatization example<\/th><\/tr><\/thead><tbody><tr><td>studies<\/td><td>studi<\/td><td>study<\/td><\/tr><tr><td>cars<\/td><td>car<\/td><td>car<\/td><\/tr><tr><td>running<\/td><td>run<\/td><td>run, when identified as a verb<\/td><\/tr><tr><td>better<\/td><td>better<\/td><td>good, when identified as an adjective<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Outputs can vary with the stemming algorithm, POS information and lexical resource. The table illustrates typical results rather than a universal mapping for every tool.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Think about your use case:<\/strong> If you are building a lightweight search index and mainly want to group similar word forms, stemming may be sufficient. If you are analysing sentence meaning or need readable base forms, lemmatization may be more appropriate. The best choice depends on what your system needs to preserve.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What is the Porter Stemmer in NLP?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The Porter stemmer is a widely used English stemming algorithm that applies a series of suffix-stripping rules. It reduces related word forms to stems, which may not be valid dictionary words.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, the NLTK Porter stemmer can reduce <em>studies<\/em> to <em>studi<\/em>. This can be useful in lightweight text preprocessing when grouping word variants matters more than producing linguistically meaningful base forms. Unlike lemmatization, the Porter stemmer does not aim to identify a dictionary lemma.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The Porter stemmer illustrates how stemming works. Now, let\u2019s see how lemmatization works in Python and how the output changes depending on the tool and grammatical information provided.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How to Perform Lemmatization in Python Using NLTK and spaCy<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">NLTK provides the WordNetLemmatizer, while spaCy supports pipeline-based lemmatization using lookup tables or rules. The appropriate method depends on your applicati<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lemmatization using NLTK<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The following NLTK lemmatization example specifies the POS for each word. The WordNetLemmatizer defaults to noun processing (pos=&#8221;n&#8221;) when no POS is supplied.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import nltk\nnltk.download(\"wordnet\")\nfrom nltk.stem import WordNetLemmatizer\nlemmatizer = WordNetLemmatizer()\nprint(lemmatizer.lemmatize(\"cars\"))\nprint(lemmatizer.lemmatize(\"running\", pos=\"v\"))\nprint(lemmatizer.lemmatize(\"better\", pos=\"a\"))<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Expected output:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">car<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">run<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">good<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The POS values used here are n for noun, v for verb and a for adjective. Without the appropriate POS, a word may remain unchanged or produce a different result. NLTK returns the input unchanged when it cannot find a suitable lemma for the specified POS.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Try changing the input:<\/strong> Run lemmatizer.lemmatize(&#8220;running&#8221;) without specifying a POS, and then compare it with lemmatizer.lemmatize(&#8220;running&#8221;, pos=&#8221;v&#8221;). Why does the second call return <em>run<\/em>? The difference illustrates why the default POS setting matters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lemmatization using spaCy<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">spaCy&#8217;s rule-based lemmatizer relies on grammatical annotations supplied by earlier pipeline components. Install spaCy and download its English model before running this example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install spacy\npython -m spacy download en_core_web_sm\nimport spacy\nnlp = spacy.load(\"en_core_web_sm\")<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">doc = nlp(&#8220;I was reading the paper.&#8221;)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">print([token.lemma_ for token in doc])<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Expected output:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[&#8216;I&#8217;, &#8216;be&#8217;, &#8216;read&#8217;, &#8216;the&#8217;, &#8216;paper&#8217;, &#8216;.&#8217;]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The pipeline identifies grammatical information and assigns lemmas to tokens. Notice that spaCy returns <em>be<\/em> for <em>was<\/em> and <em>read<\/em> for <em>reading<\/em>. If you use a different language model or lemmatizer configuration, check the output rather than assuming every pipeline will behave identically.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">spaCy also supports lookup-based approaches, so the exact setup depends on the selected language and lemmatizer mode.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">When should You Use Stemming vs Lemmatization?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Choose based on your task rather than assuming one technique is always better.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Before deciding, ask yourself three questions: Do you need valid dictionary forms? Does grammatical context matter to your task? Have you tested whether normalising the words improves your results? Your answers can help determine whether stemming, lemmatization, or neither is appropriate.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When stemming is appropriate<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Search indexing or retrieval where aggressive word reduction is acceptable.<\/li>\n\n\n\n<li>Lightweight preprocessing where speed and simplicity are priorities.<\/li>\n\n\n\n<li>Tasks where producing a valid dictionary form is less important than grouping word variants.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">When lemmatization is appropriate<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Text analysis where meaningful word forms improve interpretability.<\/li>\n\n\n\n<li>Text classification that benefits from consistent treatment of inflected forms.<\/li>\n\n\n\n<li>Information retrieval where matching grammatical variants may help.<\/li>\n\n\n\n<li>Tasks that need irregular forms or grammatical distinctions to be handled appropriately.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Always evaluate the effect on your actual dataset. Normalising words can help in some cases but can also remove distinctions that matter.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Is Lemmatization Still Relevant in the LLM Era?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization remains useful in selected NLP workflows, but modern language-model applications do not automatically need a separate lemmatization step.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is tempting to assume that more preprocessing automatically leads to better results. But consider this: if a model needs grammatical information to interpret a sentence, could converting every word to its lemma remove something useful? This is why preprocessing decisions should be tested against the task rather than applied by default.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Where Lemmatization can still help<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It may help search and retrieval systems, structured linguistic analysis, and traditional machine-learning pipelines where consistent vocabulary forms are useful. For example, a retrieval system may benefit from matching different inflections of a query term.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When it may be unnecessary<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Transformer models commonly use subword tokenisation, which splits text into units between whole words and individual characters. These models can process many word variants without requiring every word to be converted into a lemma first.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Preprocessing can also remove grammatical or semantic information that a task needs. Therefore, lemmatization should be tested against the actual objective rather than assumed to improve model accuracy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization helps NLP systems convert words into meaningful dictionary forms using lexical resources, morphology and, where supported, part-of-speech tagging. Unlike stemming, which reduces words through suffix-based rules, lemmatization aims to preserve linguistic meaning while identifying the appropriate base form. Although modern NLP and LLM pipelines do not always require a separate lemmatization step, it remains useful for selected applications such as text classification, information retrieval and linguistic analysis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To explore these concepts further, the<strong><a href=\"https:\/\/www.interviewbit.com\/varsity\/iit-delhi\/generative-ai\"> Generative AI programme by IIT Delhi<\/a><\/strong>, delivered through Varsity by InterviewBit, covers NLP foundations alongside broader generative AI concepts. These topics help learners connect linguistic processing techniques with the development of modern AI systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Does lemmatization change the meaning of a sentence?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization aims to identify a word&#8217;s base form, but incorrect POS tagging or ambiguity can produce an unsuitable result. Always check whether the normalised form preserves distinctions relevant to your task.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Can lemmatization handle irregular verbs?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes, when the relevant lexical resource or rules support the irregular form. For example, <em>went<\/em> can map to <em>go<\/em> when it is correctly identified as a verb.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Why does NLTK sometimes leave a word unchanged?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">WordNetLemmatizer defaults to noun processing and returns the input unchanged when it cannot find a suitable lemma for the specified POS. Supplying the correct POS can change the result.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Does lemmatization work for languages other than English?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Support depends on the library, language model and available lexical resources. Languages with complex morphological systems may require language-specific tools and rules.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Can two different words have the same lemma?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. Inflected forms such as <em>write<\/em>, <em>writes<\/em> and <em>wrote<\/em> can map to <em>write<\/em> when their grammatical roles are identified appropriately.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Is lemmatization supervised or unsupervised?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Lemmatization is a linguistic processing task, not one fixed learning paradigm. Implementations may use dictionaries, rules, statistical methods or trained components.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">What is the difference between a lemma and a root word?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A lemma is a word&#8217;s canonical dictionary form. A root is the core morpheme from which a word is formed. The concepts are related but are not interchangeable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Can lemmatization improve search results?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It can help match inflected forms, but its impact depends on the language, query patterns, indexing strategy and evaluation results. It should be tested against the retrieval task.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Does spaCy require POS tagging for lemmatization?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">spaCy&#8217;s rule-based lemmatizer requires prior POS annotations. Lookup-based lemmatization works differently, so the requirements depend on the selected pipeline and configuration.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\">Should I lemmatize text before passing it to an LLM?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Not necessarily. Lemmatization may help structured analysis or retrieval, but it can also remove grammatical information a language model needs. Choose preprocessing based on the task and evaluate the results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Resources<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.nltk.org\/api\/nltk.stem.wordnet.html\">NLTK WordNetLemmatizer documentation<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.nltk.org\/api\/nltk.stem.PorterStemmer.html\">NLTK PorterStemmer documentation<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/spacy.io\/api\/lemmatizer\/\">spaCy Lemmatizer API<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/spacy.io\/usage\/linguistic-features\">spaCy linguistic features documentation<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/huggingface.co\/docs\/transformers\/tokenizer_summary\">Hugging Face Transformers: Tokenization algorithms<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Key Insights Lemmatization is a natural language processing (NLP) technique that converts a word into its dictionary base form, called a lemma. For example, running can become run. By grouping related word forms, lemmatization in NLP can help systems analyse text consistently. Imagine you are building a search system that needs to recognise connect, connecting, [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":1657,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1589","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"acf":{"reviewed_by":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Lemmatization in NLP: Meaning, Examples &amp; Stemming vs Lemmatization<\/title>\n<meta name=\"description\" content=\"Learn what lemmatization in NLP means, explore examples, compare stemming vs lemmatization, and implement lemmatization in Python using NLTK and spaCy.\" \/>\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.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lemmatization in NLP: Meaning, Examples &amp; Stemming vs Lemmatization\" \/>\n<meta property=\"og:description\" content=\"Learn what lemmatization in NLP means, explore examples, compare stemming vs lemmatization, and implement lemmatization in Python using NLTK and spaCy.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/\" \/>\n<meta property=\"og:site_name\" content=\"Varsity Blog\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-22T06:23:05+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-22T07:29:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/emmatization-in-nlp.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1010\" \/>\n\t<meta property=\"og:image:height\" content=\"673\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Varsity on Behalf of CEP IIT Delhi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Varsity on Behalf of CEP IIT Delhi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/\"},\"author\":{\"name\":\"Varsity on Behalf of CEP IIT Delhi\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#\\\/schema\\\/person\\\/7b5db9a94eddee529cd35968692d9c29\"},\"headline\":\"Lemmatization in NLP: Meaning, Examples &#038; Stemming vs Lemmatization\",\"datePublished\":\"2026-10-22T06:23:05+00:00\",\"dateModified\":\"2026-10-22T07:29:37+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/\"},\"wordCount\":2185,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/emmatization-in-nlp.webp\",\"articleSection\":[\"Generative AI\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/\",\"url\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/\",\"name\":\"Lemmatization in NLP: Meaning, Examples & Stemming vs Lemmatization\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/emmatization-in-nlp.webp\",\"datePublished\":\"2026-10-22T06:23:05+00:00\",\"dateModified\":\"2026-10-22T07:29:37+00:00\",\"description\":\"Learn what lemmatization in NLP means, explore examples, compare stemming vs lemmatization, and implement lemmatization in Python using NLTK and spaCy.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/emmatization-in-nlp.webp\",\"contentUrl\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/emmatization-in-nlp.webp\",\"width\":1010,\"height\":673,\"caption\":\"lemmatization-in-nlp\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/lemmatization-in-nlp\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Lemmatization in NLP: Meaning, Examples &#038; Stemming vs Lemmatization\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/\",\"name\":\"Varsity Blog\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#organization\",\"name\":\"Varsity Blog\",\"url\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/varsity-logo.png\",\"contentUrl\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/varsity-logo.png\",\"width\":275,\"height\":64,\"caption\":\"Varsity Blog\"},\"image\":{\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/company\\\/varsity-by-interviewbit\\\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/#\\\/schema\\\/person\\\/7b5db9a94eddee529cd35968692d9c29\",\"name\":\"Varsity on Behalf of CEP IIT Delhi\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/bdb12370d043ff980b2b8f1ccb67f5a1f0e333aaca46cc35358b1af8b1d98334?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/bdb12370d043ff980b2b8f1ccb67f5a1f0e333aaca46cc35358b1af8b1d98334?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/bdb12370d043ff980b2b8f1ccb67f5a1f0e333aaca46cc35358b1af8b1d98334?s=96&d=mm&r=g\",\"caption\":\"Varsity on Behalf of CEP IIT Delhi\"},\"description\":\"Varsity by InterviewBit, in collaboration with CEP IIT Delhi, creates industry-relevant learning programmes designed to help learners build practical, in-demand skills. Through this author profile, we publish articles that complement our courses covering curriculum-aligned topics, foundational concepts, emerging trends, and advanced insights. Our goal is to help learners deepen their understanding beyond the classroom and apply their knowledge confidently in real-world contexts.\",\"url\":\"https:\\\/\\\/www.interviewbit.com\\\/varsity\\\/blog\\\/author\\\/varsity-on-behalf-of-cep-iit-delhi\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Lemmatization in NLP: Meaning, Examples & Stemming vs Lemmatization","description":"Learn what lemmatization in NLP means, explore examples, compare stemming vs lemmatization, and implement lemmatization in Python using NLTK and spaCy.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/","og_locale":"en_US","og_type":"article","og_title":"Lemmatization in NLP: Meaning, Examples & Stemming vs Lemmatization","og_description":"Learn what lemmatization in NLP means, explore examples, compare stemming vs lemmatization, and implement lemmatization in Python using NLTK and spaCy.","og_url":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/","og_site_name":"Varsity Blog","article_published_time":"2026-10-22T06:23:05+00:00","article_modified_time":"2026-10-22T07:29:37+00:00","og_image":[{"width":1010,"height":673,"url":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/emmatization-in-nlp.webp","type":"image\/webp"}],"author":"Varsity on Behalf of CEP IIT Delhi","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Varsity on Behalf of CEP IIT Delhi","Est. reading time":"11 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#article","isPartOf":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/"},"author":{"name":"Varsity on Behalf of CEP IIT Delhi","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#\/schema\/person\/7b5db9a94eddee529cd35968692d9c29"},"headline":"Lemmatization in NLP: Meaning, Examples &#038; Stemming vs Lemmatization","datePublished":"2026-10-22T06:23:05+00:00","dateModified":"2026-10-22T07:29:37+00:00","mainEntityOfPage":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/"},"wordCount":2185,"commentCount":0,"publisher":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#organization"},"image":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#primaryimage"},"thumbnailUrl":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/emmatization-in-nlp.webp","articleSection":["Generative AI"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/","url":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/","name":"Lemmatization in NLP: Meaning, Examples & Stemming vs Lemmatization","isPartOf":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#primaryimage"},"image":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#primaryimage"},"thumbnailUrl":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/emmatization-in-nlp.webp","datePublished":"2026-10-22T06:23:05+00:00","dateModified":"2026-10-22T07:29:37+00:00","description":"Learn what lemmatization in NLP means, explore examples, compare stemming vs lemmatization, and implement lemmatization in Python using NLTK and spaCy.","breadcrumb":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#primaryimage","url":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/emmatization-in-nlp.webp","contentUrl":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/10\/emmatization-in-nlp.webp","width":1010,"height":673,"caption":"lemmatization-in-nlp"},{"@type":"BreadcrumbList","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/lemmatization-in-nlp\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.interviewbit.com\/varsity\/blog\/"},{"@type":"ListItem","position":2,"name":"Lemmatization in NLP: Meaning, Examples &#038; Stemming vs Lemmatization"}]},{"@type":"WebSite","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#website","url":"https:\/\/www.interviewbit.com\/varsity\/blog\/","name":"Varsity Blog","description":"","publisher":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.interviewbit.com\/varsity\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#organization","name":"Varsity Blog","url":"https:\/\/www.interviewbit.com\/varsity\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/08\/varsity-logo.png","contentUrl":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-content\/uploads\/2026\/08\/varsity-logo.png","width":275,"height":64,"caption":"Varsity Blog"},"image":{"@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.linkedin.com\/company\/varsity-by-interviewbit\/"]},{"@type":"Person","@id":"https:\/\/www.interviewbit.com\/varsity\/blog\/#\/schema\/person\/7b5db9a94eddee529cd35968692d9c29","name":"Varsity on Behalf of CEP IIT Delhi","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/bdb12370d043ff980b2b8f1ccb67f5a1f0e333aaca46cc35358b1af8b1d98334?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/bdb12370d043ff980b2b8f1ccb67f5a1f0e333aaca46cc35358b1af8b1d98334?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/bdb12370d043ff980b2b8f1ccb67f5a1f0e333aaca46cc35358b1af8b1d98334?s=96&d=mm&r=g","caption":"Varsity on Behalf of CEP IIT Delhi"},"description":"Varsity by InterviewBit, in collaboration with CEP IIT Delhi, creates industry-relevant learning programmes designed to help learners build practical, in-demand skills. Through this author profile, we publish articles that complement our courses covering curriculum-aligned topics, foundational concepts, emerging trends, and advanced insights. Our goal is to help learners deepen their understanding beyond the classroom and apply their knowledge confidently in real-world contexts.","url":"https:\/\/www.interviewbit.com\/varsity\/blog\/author\/varsity-on-behalf-of-cep-iit-delhi\/"}]}},"_links":{"self":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts\/1589","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/comments?post=1589"}],"version-history":[{"count":3,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts\/1589\/revisions"}],"predecessor-version":[{"id":1697,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/posts\/1589\/revisions\/1697"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/media\/1657"}],"wp:attachment":[{"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/media?parent=1589"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/categories?post=1589"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.interviewbit.com\/varsity\/blog\/wp-json\/wp\/v2\/tags?post=1589"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}