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1. Shard Calculation

Query:

Response:

Calculated Shard: 103 (from laksa031)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa103.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.geeksforgeeks.org/nlp/natural-language-processing-nlp-tutorial/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.geeksforgeeks.org/nlp/natural-language-processing-nlp-tutorial/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.geeksforgeeks.org\/nlp\/natural-language-processing-nlp-tutorial\/","crawl_time":1775427307,"first_indexed_time":1751607149,"http_code":200,"src_unparsed":"org,geeksforgeeks!www,\/nlp\/natural-language-processing-nlp-tutorial\/ s443","src_root_hash":"12046344915360636903","history_drop_reason":null,"meta_title":"Natural Language Processing (NLP) Tutorial - GeeksforGeeks","meta_descriptions":["Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.","Your All-in-One Learning Portal. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice\/competitive programming\/company interview Questions."],"attrs_boilerpipe_text":"Last Updated :\n24 Feb, 2026\nNatural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization.\nPrevious\nPause\nNext\n4 \/ 4\nBasics\nNLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech.\nIntroduction to NLP\nPhases of NLP\nLibraries\nSome of natural language processing libraries include:\nNLTK (Natural Language Toolkit)\nspaCy\nTextBlob\nTransformers (by Hugging Face)\nGensim\nNLP Libraries\nText Preprocessing Techniques\nPreprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include:\nTokenization\nStopword Removal\nPunctuation Removal\nStemming\nLemmatization\nText Normalization\nParts of Speech (POS) Tagging\nParsing\nText Representation and Embedding Techniques\nMachines require numerical input, so text must be converted into numbers (vectors).\nText Representation Techniques\nIt converts textual data into numerical vectors.\nOne-Hot Encoding\nBag of Words (BOW)\nTerm Frequency-Inverse Document Frequency (TF-IDF)\nN-Gram Language Modeling\nLatent Semantic Analysis (LSA)\nLatent Dirichlet Allocation (LDA)\nText Embedding Techniques\nIt refers to methods that create dense vector representations of text that captures semantic meaning.\nWord Embedding\n:\nWord2Vec\n,\nGloVe\n,\nfastText\nPre-Trained Embedding:\nELMo\n,\nBERT\nDocument Embedding:\nDoc2Vec\nAdvanced Embeddings\n:\nRoBERTa\n,\nDistilBERT\nModel Training\nOnce text is numeric, models are trained to learn patterns and perform NLP tasks.\nTraditional Machine Learning\nNaive Bayes\nLogistic Regression\nSVM\nRandom Forest\n Deep Learning Techniques\nArtificial Neural Networks (ANNs)\nRecurrent Neural Networks (RNNs)\nLong Short-Term Memory (LSTM)\nGated Recurrent Unit (GRU)\nSeq2Seq Models\nTransformer Models\nPre-Trained Language Models\nGPT (Generative Pre-trained Transformer)\nTransformers XL\nT5 (Text-to-Text Transfer Transformer)\nTransfer Learning with Fine-tuning\nNLP Tasks\nCore NLP tasks that help machines understand, interpret and generate human language.\nText Classification:\nDataset for Text Classification\n,\nNaive Bayes\n,\nLogistic Regression\n,\nRNNs\n,\nCNNs\nInformation Extraction\nNamed Entity Recognition (NER)\n,\nNLTK,\nRelationship Extraction\n,\nWord Sense Disambiguation (WSD)\nSentiment Analysis\n:\nVADER,\nRNN\n,\nOpinion Mining\nMachine Translation\n:\nStatistical Machine Translation of Language\n,\nMachine Translation with Transformer\nText Summarization\n:\nHugging Face Model\n,\nSumy\nText Generation\n:\nFnet\n,\nLSTM\n,\nHuggingFace Model\nQuestion Answering\n:\nRetrieval-Based QA\n,\nGenerative QA\nApplications\nVoice Assistants:\nAlexa, Siri and Google Assistant use NLP for voice recognition and interaction.\nGrammar and Text Analysis:\nTools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking.\nInformation Extraction:\nSearch engines like Google and DuckDuckGo use NLP to extract relevant information.\nChatbots:\nWebsite bots and customer support chatbots leverage NLP for automated conversations.\nFor more details you can refer to:\nApplications of NLP","attrs_markdown":"[![geeksforgeeks](https:\/\/media.geeksforgeeks.org\/gfg-gg-logo.svg)](https:\/\/www.geeksforgeeks.org\/)\n\n![search icon](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/Property=Light---Default.svg)\n\n- Sign In\n- [Courses]()\n- [Tutorials]()\n- [Interview Prep]()\n\n- [NLP Tutorial](https:\/\/www.geeksforgeeks.org\/nlp\/natural-language-processing-nlp-tutorial\/)\n- [Libraries](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-libraries-in-python\/)\n- [Phases](https:\/\/www.geeksforgeeks.org\/machine-learning\/phases-of-natural-language-processing-nlp\/)\n- [Text Preprosessing](https:\/\/www.geeksforgeeks.org\/nlp\/text-preprocessing-for-nlp-tasks\/)\n- [Tokenization](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-how-tokenizing-text-sentence-words-works\/)\n- [Lemmatization](https:\/\/www.geeksforgeeks.org\/python\/python-lemmatization-with-nltk\/)\n- [Word Embeddings](https:\/\/www.geeksforgeeks.org\/nlp\/word-embeddings-in-nlp\/)\n- [Projects Ideas](https:\/\/www.geeksforgeeks.org\/nlp\/top-natural-language-processing-nlp-projects\/)\n- [Interview Question](https:\/\/www.geeksforgeeks.org\/nlp\/advanced-natural-language-processing-interview-question\/)\n- [NLP Quiz](https:\/\/www.geeksforgeeks.org\/quizzes\/natural-language-processing-quiz\/)\n\n# Natural Language Processing (NLP) Tutorial\nLast Updated : 24 Feb, 2026\n\nNatural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization.\n\n![what\\_is\\_natural\\_language\\_processing\\_.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20260224174005276626\/what_is_natural_language_processing_.webp)![what\\_is\\_natural\\_language\\_processing\\_.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20260224174005276626\/what_is_natural_language_processing_.webp)\n\n![nlp\\_techniques.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151401207177\/nlp_techniques.webp)![nlp\\_techniques.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151401207177\/nlp_techniques.webp)\n\n![nlp\\_tasks-.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400475788\/nlp_tasks-.webp)![nlp\\_tasks-.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400475788\/nlp_tasks-.webp)\n\n![nlp\\_applications.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400989786\/nlp_applications.webp)![nlp\\_applications.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400989786\/nlp_applications.webp)\n\n4 \/ 4\n## Basics\nNLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech.\n\n- [Introduction to NLP](https:\/\/www.geeksforgeeks.org\/nlp\/natural-language-processing-overview\/)\n- [Phases of NLP](https:\/\/www.geeksforgeeks.org\/machine-learning\/phases-of-natural-language-processing-nlp\/)\n## Libraries\nSome of natural language processing libraries include:\n\n- [NLTK (Natural Language Toolkit)](https:\/\/www.geeksforgeeks.org\/python\/nltk-nlp\/)\n- [spaCy](https:\/\/www.geeksforgeeks.org\/nlp\/tokenization-using-spacy-library\/)\n- [TextBlob](https:\/\/www.geeksforgeeks.org\/python\/python-textblob-sentiment-method\/)\n- [Transformers (by Hugging Face)](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/introduction-to-hugging-face-transformers\/)\n- [Gensim](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-gensim-tutorial\/)\n- [NLP Libraries](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-libraries-in-python\/)\n## Text Preprocessing Techniques\nPreprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include:\n\n- [Tokenization](https:\/\/www.geeksforgeeks.org\/nlp\/what-is-tokenization\/)\n- [Stopword Removal](https:\/\/www.geeksforgeeks.org\/nlp\/removing-stop-words-nltk-python\/)\n- [Punctuation Removal](https:\/\/www.geeksforgeeks.org\/nlp\/how-to-remove-punctuations-in-nltk\/)\n- [Stemming](https:\/\/www.geeksforgeeks.org\/machine-learning\/introduction-to-stemming\/)\n- [Lemmatization](https:\/\/www.geeksforgeeks.org\/python\/python-lemmatization-with-nltk\/)\n- [Text Normalization](https:\/\/www.geeksforgeeks.org\/python\/normalizing-textual-data-with-python\/)\n- [Parts of Speech (POS) Tagging](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-part-of-speech-default-tagging\/)\n- [Parsing](https:\/\/www.geeksforgeeks.org\/compiler-design\/introduction-of-parsing-ambiguity-and-parsers-set-1\/)\n## Text Representation and Embedding Techniques\nMachines require numerical input, so text must be converted into numbers (vectors).\n\n### ****Text Representation Techniques****\nIt converts textual data into numerical vectors.\n\n- [One-Hot Encoding](https:\/\/www.geeksforgeeks.org\/machine-learning\/ml-one-hot-encoding\/)\n- [Bag of Words (BOW)](https:\/\/www.geeksforgeeks.org\/nlp\/bag-of-words-bow-model-in-nlp\/)\n- [Term Frequency-Inverse Document Frequency (TF-IDF)](https:\/\/www.geeksforgeeks.org\/machine-learning\/understanding-tf-idf-term-frequency-inverse-document-frequency\/)\n- [N-Gram Language Modeling](https:\/\/www.geeksforgeeks.org\/nlp\/n-gram-language-modelling-with-nltk\/)\n- [Latent Semantic Analysis (LSA)](https:\/\/www.geeksforgeeks.org\/machine-learning\/latent-semantic-analysis\/)\n- [Latent Dirichlet Allocation (LDA)](https:\/\/www.geeksforgeeks.org\/machine-learning\/latent-dirichlet-allocation-and-topic-modelling\/)\n### ****Text Embedding Techniques****\nIt refers to methods that create dense vector representations of text that captures semantic meaning.\n\n- ****Word Embedding****: [Word2Vec](https:\/\/www.geeksforgeeks.org\/python\/python-word-embedding-using-word2vec\/), [GloVe](https:\/\/www.geeksforgeeks.org\/nlp\/glove-word-embedding-in-nlp\/), [fastText](https:\/\/www.geeksforgeeks.org\/nlp\/word-embeddings-using-fasttext\/)\n- ****Pre-Trained Embedding:**** [ELMo](https:\/\/www.geeksforgeeks.org\/python\/overview-of-word-embedding-using-embeddings-from-language-models-elmo\/), [BERT](https:\/\/www.geeksforgeeks.org\/nlp\/explanation-of-bert-model-nlp\/)\n- ****Document Embedding:**** [Doc2Vec](https:\/\/www.geeksforgeeks.org\/nlp\/doc2vec-in-nlp\/)\n- ****Advanced Embeddings****: [RoBERTa](https:\/\/www.geeksforgeeks.org\/machine-learning\/overview-of-roberta-model\/), [DistilBERT](https:\/\/www.geeksforgeeks.org\/nlp\/distilbert-in-natural-language-processing\/)\n## Model Training\nOnce text is numeric, models are trained to learn patterns and perform NLP tasks.\n\n### ****Traditional Machine Learning****\n- [Naive Bayes](https:\/\/www.geeksforgeeks.org\/machine-learning\/naive-bayes-classifiers\/)\n- [Logistic Regression](https:\/\/www.geeksforgeeks.org\/machine-learning\/understanding-logistic-regression\/)\n- [SVM](https:\/\/www.geeksforgeeks.org\/machine-learning\/support-vector-machine-algorithm\/)\n- [Random Forest](https:\/\/www.geeksforgeeks.org\/machine-learning\/random-forest-algorithm-in-machine-learning\/)\n### ****Deep Learning Techniques****\n- [Artificial Neural Networks (ANNs)](https:\/\/www.geeksforgeeks.org\/deep-learning\/artificial-neural-networks-and-its-applications\/)\n- [Recurrent Neural Networks (RNNs)](https:\/\/www.geeksforgeeks.org\/machine-learning\/introduction-to-recurrent-neural-network\/)\n- [Long Short-Term Memory (LSTM)](https:\/\/www.geeksforgeeks.org\/deep-learning\/deep-learning-introduction-to-long-short-term-memory\/)\n- [Gated Recurrent Unit (GRU)](https:\/\/www.geeksforgeeks.org\/machine-learning\/gated-recurrent-unit-networks\/)\n- [Seq2Seq Models](https:\/\/www.geeksforgeeks.org\/machine-learning\/seq2seq-model-in-machine-learning\/)\n- [Transformer Models](https:\/\/www.geeksforgeeks.org\/machine-learning\/getting-started-with-transformers\/)\n### Pre-Trained Language Models\n- [GPT (Generative Pre-trained Transformer)](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/introduction-to-generative-pre-trained-transformer-gpt\/)\n- [Transformers XL](https:\/\/www.geeksforgeeks.org\/nlp\/trasformer-xl-beyond-a-fixed-length-context\/)\n- [T5 (Text-to-Text Transfer Transformer)](https:\/\/www.geeksforgeeks.org\/nlp\/t5-text-to-text-transfer-transformer\/)\n- [Transfer Learning with Fine-tuning](https:\/\/www.geeksforgeeks.org\/nlp\/transfer-learning-and-fine-tuning-in-nlp\/)\n## NLP Tasks\nCore NLP tasks that help machines understand, interpret and generate human language.\n\n- ****Text Classification:**** [Dataset for Text Classification](https:\/\/www.geeksforgeeks.org\/nlp\/dataset-for-text-classification\/), [Naive Bayes](https:\/\/www.geeksforgeeks.org\/machine-learning\/classification-of-text-documents-using-the-approach-of-naive-bayes\/), [Logistic Regression](https:\/\/www.geeksforgeeks.org\/machine-learning\/text-classification-using-logistic-regression\/), [RNNs](https:\/\/www.geeksforgeeks.org\/nlp\/rnn-for-text-classifications-in-nlp\/), [CNNs](https:\/\/www.geeksforgeeks.org\/nlp\/text-classification-using-cnn\/)\n- ****Information Extraction**** [Named Entity Recognition (NER)](https:\/\/www.geeksforgeeks.org\/python\/python-named-entity-recognition-ner-using-spacy\/), [NLTK,](https:\/\/www.geeksforgeeks.org\/nlp\/named-entity-recognition\/) [Relationship Extraction](https:\/\/www.geeksforgeeks.org\/nlp\/relationship-extraction-in-nlp\/), [Word Sense Disambiguation (WSD)](https:\/\/www.geeksforgeeks.org\/machine-learning\/word-sense-disambiguation-in-natural-language-processing\/)\n- ****Sentiment Analysis****: [VADER,](https:\/\/www.geeksforgeeks.org\/python\/python-sentiment-analysis-using-vader\/) [RNN](https:\/\/www.geeksforgeeks.org\/python\/sentiment-analysis-with-an-recurrent-neural-networks-rnn\/), [Opinion Mining](https:\/\/www.geeksforgeeks.org\/nlp\/opinion-mining-in-nlp\/)\n- ****Machine Translation****: [Statistical Machine Translation of Language](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/statistical-machine-translation-of-languages-in-artificial-intelligence\/), [Machine Translation with Transformer](https:\/\/www.geeksforgeeks.org\/nlp\/machine-translation-with-transformer-in-python\/)\n- ****Text Summarization****: [Hugging Face Model](https:\/\/www.geeksforgeeks.org\/nlp\/text-summarizations-using-huggingface-model\/), [Sumy](https:\/\/www.geeksforgeeks.org\/nlp\/mastering-text-summarization-with-sumy-a-python-library-overview\/)\n- ****Text Generation****: [Fnet](https:\/\/www.geeksforgeeks.org\/nlp\/text-generation-using-fnet\/), [LSTM](https:\/\/www.geeksforgeeks.org\/machine-learning\/text-generation-using-recurrent-long-short-term-memory-network\/), [HuggingFace Model](https:\/\/www.geeksforgeeks.org\/nlp\/text2text-generations-using-huggingface-model\/)\n- ****Question Answering****: [Retrieval-Based QA](https:\/\/www.geeksforgeeks.org\/nlp\/what-is-retrieval-augmented-generation-rag\/), [Generative QA](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/data-management-in-generative-ai\/)\n## Applications\n- ****Voice Assistants:**** Alexa, Siri and Google Assistant use NLP for voice recognition and interaction.\n- ****Grammar and Text Analysis:**** Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking.\n- ****Information Extraction:**** Search engines like Google and DuckDuckGo use NLP to extract relevant information.\n- ****Chatbots:**** Website bots and customer support chatbots leverage NLP for automated conversations.\n\n> For more details you can refer to: [Applications of NLP](https:\/\/www.geeksforgeeks.org\/nlp\/top-7-applications-of-natural-language-processing\/)\n\nComment\n\n[A](https:\/\/www.geeksforgeeks.org\/user\/abhishek1\/)\n\n[abhishek1](https:\/\/www.geeksforgeeks.org\/user\/abhishek1\/)\n\n67\n\nArticle Tags:\n\nArticle Tags:\n\n[NLP](https:\/\/www.geeksforgeeks.org\/category\/ai-ml-ds\/nlp\/)\n\n[Natural-language-processing](https:\/\/www.geeksforgeeks.org\/tag\/natural-language-processing\/)\n\n[python](https:\/\/www.geeksforgeeks.org\/tag\/python\/)\n### Explore\n\n[![GeeksforGeeks](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/gfgFooterLogo.png)](https:\/\/www.geeksforgeeks.org\/)\n\n![location](https:\/\/media.geeksforgeeks.org\/img-practice\/Location-1685004904.svg)\n\nCorporate & Communications Address:\n\nA-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)\n\n![location](https:\/\/media.geeksforgeeks.org\/img-practice\/Location-1685004904.svg)\n\nRegistered 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reserved](https:\/\/www.geeksforgeeks.org\/copyright-information\/)\n\n![]()","attrs_readable_markdown":"Last Updated : 24 Feb, 2026\n\nNatural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization.\n\n![what\\_is\\_natural\\_language\\_processing\\_.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20260224174005276626\/what_is_natural_language_processing_.webp)![what\\_is\\_natural\\_language\\_processing\\_.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20260224174005276626\/what_is_natural_language_processing_.webp)\n\n![nlp\\_techniques.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151401207177\/nlp_techniques.webp)![nlp\\_techniques.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151401207177\/nlp_techniques.webp)\n\n![nlp\\_tasks-.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400475788\/nlp_tasks-.webp)![nlp\\_tasks-.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400475788\/nlp_tasks-.webp)\n\n![nlp\\_applications.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400989786\/nlp_applications.webp)![nlp\\_applications.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251215151400989786\/nlp_applications.webp)\n\n4 \/ 4\n## Basics\nNLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech.\n\n- [Introduction to NLP](https:\/\/www.geeksforgeeks.org\/nlp\/natural-language-processing-overview\/)\n- [Phases of NLP](https:\/\/www.geeksforgeeks.org\/machine-learning\/phases-of-natural-language-processing-nlp\/)\n## Libraries\nSome of natural language processing libraries include:\n\n- [NLTK (Natural Language Toolkit)](https:\/\/www.geeksforgeeks.org\/python\/nltk-nlp\/)\n- [spaCy](https:\/\/www.geeksforgeeks.org\/nlp\/tokenization-using-spacy-library\/)\n- [TextBlob](https:\/\/www.geeksforgeeks.org\/python\/python-textblob-sentiment-method\/)\n- [Transformers (by Hugging Face)](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/introduction-to-hugging-face-transformers\/)\n- [Gensim](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-gensim-tutorial\/)\n- [NLP Libraries](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-libraries-in-python\/)\n## Text Preprocessing Techniques\nPreprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include:\n\n- [Tokenization](https:\/\/www.geeksforgeeks.org\/nlp\/what-is-tokenization\/)\n- [Stopword Removal](https:\/\/www.geeksforgeeks.org\/nlp\/removing-stop-words-nltk-python\/)\n- [Punctuation Removal](https:\/\/www.geeksforgeeks.org\/nlp\/how-to-remove-punctuations-in-nltk\/)\n- [Stemming](https:\/\/www.geeksforgeeks.org\/machine-learning\/introduction-to-stemming\/)\n- [Lemmatization](https:\/\/www.geeksforgeeks.org\/python\/python-lemmatization-with-nltk\/)\n- [Text Normalization](https:\/\/www.geeksforgeeks.org\/python\/normalizing-textual-data-with-python\/)\n- [Parts of Speech (POS) Tagging](https:\/\/www.geeksforgeeks.org\/nlp\/nlp-part-of-speech-default-tagging\/)\n- [Parsing](https:\/\/www.geeksforgeeks.org\/compiler-design\/introduction-of-parsing-ambiguity-and-parsers-set-1\/)\n## Text Representation and Embedding Techniques\nMachines require numerical input, so text must be converted into numbers (vectors).\n\n### ****Text Representation Techniques****\nIt converts textual data into numerical vectors.\n\n- [One-Hot Encoding](https:\/\/www.geeksforgeeks.org\/machine-learning\/ml-one-hot-encoding\/)\n- [Bag of Words (BOW)](https:\/\/www.geeksforgeeks.org\/nlp\/bag-of-words-bow-model-in-nlp\/)\n- [Term Frequency-Inverse Document Frequency (TF-IDF)](https:\/\/www.geeksforgeeks.org\/machine-learning\/understanding-tf-idf-term-frequency-inverse-document-frequency\/)\n- [N-Gram Language Modeling](https:\/\/www.geeksforgeeks.org\/nlp\/n-gram-language-modelling-with-nltk\/)\n- [Latent Semantic Analysis (LSA)](https:\/\/www.geeksforgeeks.org\/machine-learning\/latent-semantic-analysis\/)\n- [Latent Dirichlet Allocation (LDA)](https:\/\/www.geeksforgeeks.org\/machine-learning\/latent-dirichlet-allocation-and-topic-modelling\/)\n### ****Text Embedding Techniques****\nIt refers to methods that create dense vector representations of text that captures semantic meaning.\n\n- ****Word Embedding****: [Word2Vec](https:\/\/www.geeksforgeeks.org\/python\/python-word-embedding-using-word2vec\/), [GloVe](https:\/\/www.geeksforgeeks.org\/nlp\/glove-word-embedding-in-nlp\/), [fastText](https:\/\/www.geeksforgeeks.org\/nlp\/word-embeddings-using-fasttext\/)\n- ****Pre-Trained Embedding:**** [ELMo](https:\/\/www.geeksforgeeks.org\/python\/overview-of-word-embedding-using-embeddings-from-language-models-elmo\/), [BERT](https:\/\/www.geeksforgeeks.org\/nlp\/explanation-of-bert-model-nlp\/)\n- ****Document Embedding:**** [Doc2Vec](https:\/\/www.geeksforgeeks.org\/nlp\/doc2vec-in-nlp\/)\n- ****Advanced Embeddings****: [RoBERTa](https:\/\/www.geeksforgeeks.org\/machine-learning\/overview-of-roberta-model\/), [DistilBERT](https:\/\/www.geeksforgeeks.org\/nlp\/distilbert-in-natural-language-processing\/)\n## Model Training\nOnce text is numeric, models are trained to learn patterns and perform NLP tasks.\n\n### ****Traditional Machine Learning****\n- [Naive Bayes](https:\/\/www.geeksforgeeks.org\/machine-learning\/naive-bayes-classifiers\/)\n- [Logistic Regression](https:\/\/www.geeksforgeeks.org\/machine-learning\/understanding-logistic-regression\/)\n- [SVM](https:\/\/www.geeksforgeeks.org\/machine-learning\/support-vector-machine-algorithm\/)\n- [Random Forest](https:\/\/www.geeksforgeeks.org\/machine-learning\/random-forest-algorithm-in-machine-learning\/)\n### ****Deep Learning Techniques****\n- [Artificial Neural Networks (ANNs)](https:\/\/www.geeksforgeeks.org\/deep-learning\/artificial-neural-networks-and-its-applications\/)\n- [Recurrent Neural Networks (RNNs)](https:\/\/www.geeksforgeeks.org\/machine-learning\/introduction-to-recurrent-neural-network\/)\n- [Long Short-Term Memory (LSTM)](https:\/\/www.geeksforgeeks.org\/deep-learning\/deep-learning-introduction-to-long-short-term-memory\/)\n- [Gated Recurrent Unit (GRU)](https:\/\/www.geeksforgeeks.org\/machine-learning\/gated-recurrent-unit-networks\/)\n- [Seq2Seq Models](https:\/\/www.geeksforgeeks.org\/machine-learning\/seq2seq-model-in-machine-learning\/)\n- [Transformer Models](https:\/\/www.geeksforgeeks.org\/machine-learning\/getting-started-with-transformers\/)\n### Pre-Trained Language Models\n- [GPT (Generative Pre-trained Transformer)](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/introduction-to-generative-pre-trained-transformer-gpt\/)\n- [Transformers XL](https:\/\/www.geeksforgeeks.org\/nlp\/trasformer-xl-beyond-a-fixed-length-context\/)\n- [T5 (Text-to-Text Transfer Transformer)](https:\/\/www.geeksforgeeks.org\/nlp\/t5-text-to-text-transfer-transformer\/)\n- [Transfer Learning with Fine-tuning](https:\/\/www.geeksforgeeks.org\/nlp\/transfer-learning-and-fine-tuning-in-nlp\/)\n## NLP Tasks\nCore NLP tasks that help machines understand, interpret and generate human language.\n\n- ****Text Classification:**** [Dataset for Text Classification](https:\/\/www.geeksforgeeks.org\/nlp\/dataset-for-text-classification\/), [Naive Bayes](https:\/\/www.geeksforgeeks.org\/machine-learning\/classification-of-text-documents-using-the-approach-of-naive-bayes\/), [Logistic Regression](https:\/\/www.geeksforgeeks.org\/machine-learning\/text-classification-using-logistic-regression\/), [RNNs](https:\/\/www.geeksforgeeks.org\/nlp\/rnn-for-text-classifications-in-nlp\/), [CNNs](https:\/\/www.geeksforgeeks.org\/nlp\/text-classification-using-cnn\/)\n- ****Information Extraction**** [Named Entity Recognition (NER)](https:\/\/www.geeksforgeeks.org\/python\/python-named-entity-recognition-ner-using-spacy\/), [NLTK,](https:\/\/www.geeksforgeeks.org\/nlp\/named-entity-recognition\/) [Relationship Extraction](https:\/\/www.geeksforgeeks.org\/nlp\/relationship-extraction-in-nlp\/), [Word Sense Disambiguation (WSD)](https:\/\/www.geeksforgeeks.org\/machine-learning\/word-sense-disambiguation-in-natural-language-processing\/)\n- ****Sentiment Analysis****: [VADER,](https:\/\/www.geeksforgeeks.org\/python\/python-sentiment-analysis-using-vader\/) [RNN](https:\/\/www.geeksforgeeks.org\/python\/sentiment-analysis-with-an-recurrent-neural-networks-rnn\/), [Opinion Mining](https:\/\/www.geeksforgeeks.org\/nlp\/opinion-mining-in-nlp\/)\n- ****Machine Translation****: [Statistical Machine Translation of Language](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/statistical-machine-translation-of-languages-in-artificial-intelligence\/), [Machine Translation with Transformer](https:\/\/www.geeksforgeeks.org\/nlp\/machine-translation-with-transformer-in-python\/)\n- ****Text Summarization****: [Hugging Face Model](https:\/\/www.geeksforgeeks.org\/nlp\/text-summarizations-using-huggingface-model\/), [Sumy](https:\/\/www.geeksforgeeks.org\/nlp\/mastering-text-summarization-with-sumy-a-python-library-overview\/)\n- ****Text Generation****: [Fnet](https:\/\/www.geeksforgeeks.org\/nlp\/text-generation-using-fnet\/), [LSTM](https:\/\/www.geeksforgeeks.org\/machine-learning\/text-generation-using-recurrent-long-short-term-memory-network\/), [HuggingFace Model](https:\/\/www.geeksforgeeks.org\/nlp\/text2text-generations-using-huggingface-model\/)\n- ****Question Answering****: [Retrieval-Based QA](https:\/\/www.geeksforgeeks.org\/nlp\/what-is-retrieval-augmented-generation-rag\/), [Generative QA](https:\/\/www.geeksforgeeks.org\/artificial-intelligence\/data-management-in-generative-ai\/)\n## Applications\n- ****Voice Assistants:**** Alexa, Siri and Google Assistant use NLP for voice recognition and interaction.\n- ****Grammar and Text Analysis:**** Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking.\n- ****Information Extraction:**** Search engines like Google and DuckDuckGo use NLP to extract relevant information.\n- ****Chatbots:**** Website bots and customer support chatbots leverage NLP for automated conversations.\n\n> For more details you can refer to: [Applications of NLP](https:\/\/www.geeksforgeeks.org\/nlp\/top-7-applications-of-natural-language-processing\/)","meta_canonical":null}

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Boilerpipe Text	Last Updated : 24 Feb, 2026 Natural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization. Previous Pause Next 4 / 4 Basics NLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech. Introduction to NLP Phases of NLP Libraries Some of natural language processing libraries include: NLTK (Natural Language Toolkit) spaCy TextBlob Transformers (by Hugging Face) Gensim NLP Libraries Text Preprocessing Techniques Preprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include: Tokenization Stopword Removal Punctuation Removal Stemming Lemmatization Text Normalization Parts of Speech (POS) Tagging Parsing Text Representation and Embedding Techniques Machines require numerical input, so text must be converted into numbers (vectors). Text Representation Techniques It converts textual data into numerical vectors. One-Hot Encoding Bag of Words (BOW) Term Frequency-Inverse Document Frequency (TF-IDF) N-Gram Language Modeling Latent Semantic Analysis (LSA) Latent Dirichlet Allocation (LDA) Text Embedding Techniques It refers to methods that create dense vector representations of text that captures semantic meaning. Word Embedding : Word2Vec , GloVe , fastText Pre-Trained Embedding: ELMo , BERT Document Embedding: Doc2Vec Advanced Embeddings : RoBERTa , DistilBERT Model Training Once text is numeric, models are trained to learn patterns and perform NLP tasks. Traditional Machine Learning Naive Bayes Logistic Regression SVM Random Forest Deep Learning Techniques Artificial Neural Networks (ANNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) Seq2Seq Models Transformer Models Pre-Trained Language Models GPT (Generative Pre-trained Transformer) Transformers XL T5 (Text-to-Text Transfer Transformer) Transfer Learning with Fine-tuning NLP Tasks Core NLP tasks that help machines understand, interpret and generate human language. Text Classification: Dataset for Text Classification , Naive Bayes , Logistic Regression , RNNs , CNNs Information Extraction Named Entity Recognition (NER) , NLTK, Relationship Extraction , Word Sense Disambiguation (WSD) Sentiment Analysis : VADER, RNN , Opinion Mining Machine Translation : Statistical Machine Translation of Language , Machine Translation with Transformer Text Summarization : Hugging Face Model , Sumy Text Generation : Fnet , LSTM , HuggingFace Model Question Answering : Retrieval-Based QA , Generative QA Applications Voice Assistants: Alexa, Siri and Google Assistant use NLP for voice recognition and interaction. Grammar and Text Analysis: Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking. Information Extraction: Search engines like Google and DuckDuckGo use NLP to extract relevant information. Chatbots: Website bots and customer support chatbots leverage NLP for automated conversations. For more details you can refer to: Applications of NLP
Markdown	[![geeksforgeeks](https://media.geeksforgeeks.org/gfg-gg-logo.svg)](https://www.geeksforgeeks.org/) ![search icon](https://media.geeksforgeeks.org/auth-dashboard-uploads/Property=Light---Default.svg) - Sign In - [Courses]() - [Tutorials]() - [Interview Prep]() - [NLP Tutorial](https://www.geeksforgeeks.org/nlp/natural-language-processing-nlp-tutorial/) - [Libraries](https://www.geeksforgeeks.org/nlp/nlp-libraries-in-python/) - [Phases](https://www.geeksforgeeks.org/machine-learning/phases-of-natural-language-processing-nlp/) - [Text Preprosessing](https://www.geeksforgeeks.org/nlp/text-preprocessing-for-nlp-tasks/) - [Tokenization](https://www.geeksforgeeks.org/nlp/nlp-how-tokenizing-text-sentence-words-works/) - [Lemmatization](https://www.geeksforgeeks.org/python/python-lemmatization-with-nltk/) - [Word Embeddings](https://www.geeksforgeeks.org/nlp/word-embeddings-in-nlp/) - [Projects Ideas](https://www.geeksforgeeks.org/nlp/top-natural-language-processing-nlp-projects/) - [Interview Question](https://www.geeksforgeeks.org/nlp/advanced-natural-language-processing-interview-question/) - [NLP Quiz](https://www.geeksforgeeks.org/quizzes/natural-language-processing-quiz/) # Natural Language Processing (NLP) Tutorial Last Updated : 24 Feb, 2026 Natural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization. ![what\_is\_natural\_language\_processing\_.webp](https://media.geeksforgeeks.org/wp-content/uploads/20260224174005276626/what_is_natural_language_processing_.webp)![what\_is\_natural\_language\_processing\_.webp](https://media.geeksforgeeks.org/wp-content/uploads/20260224174005276626/what_is_natural_language_processing_.webp) ![nlp\_techniques.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151401207177/nlp_techniques.webp)![nlp\_techniques.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151401207177/nlp_techniques.webp) ![nlp\_tasks-.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400475788/nlp_tasks-.webp)![nlp\_tasks-.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400475788/nlp_tasks-.webp) ![nlp\_applications.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400989786/nlp_applications.webp)![nlp\_applications.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400989786/nlp_applications.webp) 4 / 4 ## Basics NLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech. - [Introduction to NLP](https://www.geeksforgeeks.org/nlp/natural-language-processing-overview/) - [Phases of NLP](https://www.geeksforgeeks.org/machine-learning/phases-of-natural-language-processing-nlp/) ## Libraries Some of natural language processing libraries include: - [NLTK (Natural Language Toolkit)](https://www.geeksforgeeks.org/python/nltk-nlp/) - [spaCy](https://www.geeksforgeeks.org/nlp/tokenization-using-spacy-library/) - [TextBlob](https://www.geeksforgeeks.org/python/python-textblob-sentiment-method/) - [Transformers (by Hugging Face)](https://www.geeksforgeeks.org/artificial-intelligence/introduction-to-hugging-face-transformers/) - [Gensim](https://www.geeksforgeeks.org/nlp/nlp-gensim-tutorial/) - [NLP Libraries](https://www.geeksforgeeks.org/nlp/nlp-libraries-in-python/) ## Text Preprocessing Techniques Preprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include: - [Tokenization](https://www.geeksforgeeks.org/nlp/what-is-tokenization/) - [Stopword Removal](https://www.geeksforgeeks.org/nlp/removing-stop-words-nltk-python/) - [Punctuation Removal](https://www.geeksforgeeks.org/nlp/how-to-remove-punctuations-in-nltk/) - [Stemming](https://www.geeksforgeeks.org/machine-learning/introduction-to-stemming/) - [Lemmatization](https://www.geeksforgeeks.org/python/python-lemmatization-with-nltk/) - [Text Normalization](https://www.geeksforgeeks.org/python/normalizing-textual-data-with-python/) - [Parts of Speech (POS) Tagging](https://www.geeksforgeeks.org/nlp/nlp-part-of-speech-default-tagging/) - [Parsing](https://www.geeksforgeeks.org/compiler-design/introduction-of-parsing-ambiguity-and-parsers-set-1/) ## Text Representation and Embedding Techniques Machines require numerical input, so text must be converted into numbers (vectors). ### **Text Representation Techniques It converts textual data into numerical vectors. - [One-Hot Encoding](https://www.geeksforgeeks.org/machine-learning/ml-one-hot-encoding/) - [Bag of Words (BOW)](https://www.geeksforgeeks.org/nlp/bag-of-words-bow-model-in-nlp/) - [Term Frequency-Inverse Document Frequency (TF-IDF)](https://www.geeksforgeeks.org/machine-learning/understanding-tf-idf-term-frequency-inverse-document-frequency/) - [N-Gram Language Modeling](https://www.geeksforgeeks.org/nlp/n-gram-language-modelling-with-nltk/) - [Latent Semantic Analysis (LSA)](https://www.geeksforgeeks.org/machine-learning/latent-semantic-analysis/) - [Latent Dirichlet Allocation (LDA)](https://www.geeksforgeeks.org/machine-learning/latent-dirichlet-allocation-and-topic-modelling/) ### Text Embedding Techniques It refers to methods that create dense vector representations of text that captures semantic meaning. - Word Embedding: [Word2Vec](https://www.geeksforgeeks.org/python/python-word-embedding-using-word2vec/), [GloVe](https://www.geeksforgeeks.org/nlp/glove-word-embedding-in-nlp/), [fastText](https://www.geeksforgeeks.org/nlp/word-embeddings-using-fasttext/) - Pre-Trained Embedding: [ELMo](https://www.geeksforgeeks.org/python/overview-of-word-embedding-using-embeddings-from-language-models-elmo/), [BERT](https://www.geeksforgeeks.org/nlp/explanation-of-bert-model-nlp/) - Document Embedding: [Doc2Vec](https://www.geeksforgeeks.org/nlp/doc2vec-in-nlp/) - Advanced Embeddings: [RoBERTa](https://www.geeksforgeeks.org/machine-learning/overview-of-roberta-model/), [DistilBERT](https://www.geeksforgeeks.org/nlp/distilbert-in-natural-language-processing/) ## Model Training Once text is numeric, models are trained to learn patterns and perform NLP tasks. ### Traditional Machine Learning - [Naive Bayes](https://www.geeksforgeeks.org/machine-learning/naive-bayes-classifiers/) - [Logistic Regression](https://www.geeksforgeeks.org/machine-learning/understanding-logistic-regression/) - [SVM](https://www.geeksforgeeks.org/machine-learning/support-vector-machine-algorithm/) - [Random Forest](https://www.geeksforgeeks.org/machine-learning/random-forest-algorithm-in-machine-learning/) ### Deep Learning Techniques - [Artificial Neural Networks (ANNs)](https://www.geeksforgeeks.org/deep-learning/artificial-neural-networks-and-its-applications/) - [Recurrent Neural Networks (RNNs)](https://www.geeksforgeeks.org/machine-learning/introduction-to-recurrent-neural-network/) - [Long Short-Term Memory (LSTM)](https://www.geeksforgeeks.org/deep-learning/deep-learning-introduction-to-long-short-term-memory/) - [Gated Recurrent Unit (GRU)](https://www.geeksforgeeks.org/machine-learning/gated-recurrent-unit-networks/) - [Seq2Seq Models](https://www.geeksforgeeks.org/machine-learning/seq2seq-model-in-machine-learning/) - [Transformer Models](https://www.geeksforgeeks.org/machine-learning/getting-started-with-transformers/) ### Pre-Trained Language Models - [GPT (Generative Pre-trained Transformer)](https://www.geeksforgeeks.org/artificial-intelligence/introduction-to-generative-pre-trained-transformer-gpt/) - [Transformers XL](https://www.geeksforgeeks.org/nlp/trasformer-xl-beyond-a-fixed-length-context/) - [T5 (Text-to-Text Transfer Transformer)](https://www.geeksforgeeks.org/nlp/t5-text-to-text-transfer-transformer/) - [Transfer Learning with Fine-tuning](https://www.geeksforgeeks.org/nlp/transfer-learning-and-fine-tuning-in-nlp/) ## NLP Tasks Core NLP tasks that help machines understand, interpret and generate human language. - Text Classification: [Dataset for Text Classification](https://www.geeksforgeeks.org/nlp/dataset-for-text-classification/), [Naive Bayes](https://www.geeksforgeeks.org/machine-learning/classification-of-text-documents-using-the-approach-of-naive-bayes/), [Logistic Regression](https://www.geeksforgeeks.org/machine-learning/text-classification-using-logistic-regression/), [RNNs](https://www.geeksforgeeks.org/nlp/rnn-for-text-classifications-in-nlp/), [CNNs](https://www.geeksforgeeks.org/nlp/text-classification-using-cnn/) - Information Extraction [Named Entity Recognition (NER)](https://www.geeksforgeeks.org/python/python-named-entity-recognition-ner-using-spacy/), [NLTK,](https://www.geeksforgeeks.org/nlp/named-entity-recognition/) [Relationship Extraction](https://www.geeksforgeeks.org/nlp/relationship-extraction-in-nlp/), [Word Sense Disambiguation (WSD)](https://www.geeksforgeeks.org/machine-learning/word-sense-disambiguation-in-natural-language-processing/) - Sentiment Analysis: [VADER,](https://www.geeksforgeeks.org/python/python-sentiment-analysis-using-vader/) [RNN](https://www.geeksforgeeks.org/python/sentiment-analysis-with-an-recurrent-neural-networks-rnn/), [Opinion Mining](https://www.geeksforgeeks.org/nlp/opinion-mining-in-nlp/) - Machine Translation: [Statistical Machine Translation of Language](https://www.geeksforgeeks.org/artificial-intelligence/statistical-machine-translation-of-languages-in-artificial-intelligence/), [Machine Translation with Transformer](https://www.geeksforgeeks.org/nlp/machine-translation-with-transformer-in-python/) - Text Summarization: [Hugging Face Model](https://www.geeksforgeeks.org/nlp/text-summarizations-using-huggingface-model/), [Sumy](https://www.geeksforgeeks.org/nlp/mastering-text-summarization-with-sumy-a-python-library-overview/) - Text Generation: [Fnet](https://www.geeksforgeeks.org/nlp/text-generation-using-fnet/), [LSTM](https://www.geeksforgeeks.org/machine-learning/text-generation-using-recurrent-long-short-term-memory-network/), [HuggingFace Model](https://www.geeksforgeeks.org/nlp/text2text-generations-using-huggingface-model/) - Question Answering: [Retrieval-Based QA](https://www.geeksforgeeks.org/nlp/what-is-retrieval-augmented-generation-rag/), [Generative QA](https://www.geeksforgeeks.org/artificial-intelligence/data-management-in-generative-ai/) ## Applications - Voice Assistants: Alexa, Siri and Google Assistant use NLP for voice recognition and interaction. - Grammar and Text Analysis: Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking. - Information Extraction: Search engines like Google and DuckDuckGo use NLP to extract relevant information. - Chatbots:** Website bots and customer support chatbots leverage NLP for automated conversations. > For more details you can refer to: [Applications of NLP](https://www.geeksforgeeks.org/nlp/top-7-applications-of-natural-language-processing/) Comment [A](https://www.geeksforgeeks.org/user/abhishek1/) [abhishek1](https://www.geeksforgeeks.org/user/abhishek1/) 67 Article Tags: Article Tags: [NLP](https://www.geeksforgeeks.org/category/ai-ml-ds/nlp/) [Natural-language-processing](https://www.geeksforgeeks.org/tag/natural-language-processing/) [python](https://www.geeksforgeeks.org/tag/python/) ### Explore [![GeeksforGeeks](https://media.geeksforgeeks.org/auth-dashboard-uploads/gfgFooterLogo.png)](https://www.geeksforgeeks.org/) ![location](https://media.geeksforgeeks.org/img-practice/Location-1685004904.svg) Corporate & Communications Address: A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305) ![location](https://media.geeksforgeeks.org/img-practice/Location-1685004904.svg) Registered Address: K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305 [![GFG App on Play Store](https://media.geeksforgeeks.org/auth-dashboard-uploads/googleplay-%281%29.png)](https://geeksforgeeksapp.page.link/gfg-app)[![GFG App on App Store](https://media.geeksforgeeks.org/auth-dashboard-uploads/appstore-%281%29.png)](https://geeksforgeeksapp.page.link/gfg-app) - 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Readable Markdown	Last Updated : 24 Feb, 2026 Natural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization. ![what\_is\_natural\_language\_processing\_.webp](https://media.geeksforgeeks.org/wp-content/uploads/20260224174005276626/what_is_natural_language_processing_.webp)![what\_is\_natural\_language\_processing\_.webp](https://media.geeksforgeeks.org/wp-content/uploads/20260224174005276626/what_is_natural_language_processing_.webp) ![nlp\_techniques.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151401207177/nlp_techniques.webp)![nlp\_techniques.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151401207177/nlp_techniques.webp) ![nlp\_tasks-.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400475788/nlp_tasks-.webp)![nlp\_tasks-.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400475788/nlp_tasks-.webp) ![nlp\_applications.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400989786/nlp_applications.webp)![nlp\_applications.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251215151400989786/nlp_applications.webp) 4 / 4 ## Basics NLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech. - [Introduction to NLP](https://www.geeksforgeeks.org/nlp/natural-language-processing-overview/) - [Phases of NLP](https://www.geeksforgeeks.org/machine-learning/phases-of-natural-language-processing-nlp/) ## Libraries Some of natural language processing libraries include: - [NLTK (Natural Language Toolkit)](https://www.geeksforgeeks.org/python/nltk-nlp/) - [spaCy](https://www.geeksforgeeks.org/nlp/tokenization-using-spacy-library/) - [TextBlob](https://www.geeksforgeeks.org/python/python-textblob-sentiment-method/) - [Transformers (by Hugging Face)](https://www.geeksforgeeks.org/artificial-intelligence/introduction-to-hugging-face-transformers/) - [Gensim](https://www.geeksforgeeks.org/nlp/nlp-gensim-tutorial/) - [NLP Libraries](https://www.geeksforgeeks.org/nlp/nlp-libraries-in-python/) ## Text Preprocessing Techniques Preprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include: - [Tokenization](https://www.geeksforgeeks.org/nlp/what-is-tokenization/) - [Stopword Removal](https://www.geeksforgeeks.org/nlp/removing-stop-words-nltk-python/) - [Punctuation Removal](https://www.geeksforgeeks.org/nlp/how-to-remove-punctuations-in-nltk/) - [Stemming](https://www.geeksforgeeks.org/machine-learning/introduction-to-stemming/) - [Lemmatization](https://www.geeksforgeeks.org/python/python-lemmatization-with-nltk/) - [Text Normalization](https://www.geeksforgeeks.org/python/normalizing-textual-data-with-python/) - [Parts of Speech (POS) Tagging](https://www.geeksforgeeks.org/nlp/nlp-part-of-speech-default-tagging/) - [Parsing](https://www.geeksforgeeks.org/compiler-design/introduction-of-parsing-ambiguity-and-parsers-set-1/) ## Text Representation and Embedding Techniques Machines require numerical input, so text must be converted into numbers (vectors). ### **Text Representation Techniques It converts textual data into numerical vectors. - [One-Hot Encoding](https://www.geeksforgeeks.org/machine-learning/ml-one-hot-encoding/) - [Bag of Words (BOW)](https://www.geeksforgeeks.org/nlp/bag-of-words-bow-model-in-nlp/) - [Term Frequency-Inverse Document Frequency (TF-IDF)](https://www.geeksforgeeks.org/machine-learning/understanding-tf-idf-term-frequency-inverse-document-frequency/) - [N-Gram Language Modeling](https://www.geeksforgeeks.org/nlp/n-gram-language-modelling-with-nltk/) - [Latent Semantic Analysis (LSA)](https://www.geeksforgeeks.org/machine-learning/latent-semantic-analysis/) - [Latent Dirichlet Allocation (LDA)](https://www.geeksforgeeks.org/machine-learning/latent-dirichlet-allocation-and-topic-modelling/) ### Text Embedding Techniques It refers to methods that create dense vector representations of text that captures semantic meaning. - Word Embedding: [Word2Vec](https://www.geeksforgeeks.org/python/python-word-embedding-using-word2vec/), [GloVe](https://www.geeksforgeeks.org/nlp/glove-word-embedding-in-nlp/), [fastText](https://www.geeksforgeeks.org/nlp/word-embeddings-using-fasttext/) - Pre-Trained Embedding: [ELMo](https://www.geeksforgeeks.org/python/overview-of-word-embedding-using-embeddings-from-language-models-elmo/), [BERT](https://www.geeksforgeeks.org/nlp/explanation-of-bert-model-nlp/) - Document Embedding: [Doc2Vec](https://www.geeksforgeeks.org/nlp/doc2vec-in-nlp/) - Advanced Embeddings: [RoBERTa](https://www.geeksforgeeks.org/machine-learning/overview-of-roberta-model/), [DistilBERT](https://www.geeksforgeeks.org/nlp/distilbert-in-natural-language-processing/) ## Model Training Once text is numeric, models are trained to learn patterns and perform NLP tasks. ### Traditional Machine Learning - [Naive Bayes](https://www.geeksforgeeks.org/machine-learning/naive-bayes-classifiers/) - [Logistic Regression](https://www.geeksforgeeks.org/machine-learning/understanding-logistic-regression/) - [SVM](https://www.geeksforgeeks.org/machine-learning/support-vector-machine-algorithm/) - [Random Forest](https://www.geeksforgeeks.org/machine-learning/random-forest-algorithm-in-machine-learning/) ### Deep Learning Techniques - [Artificial Neural Networks (ANNs)](https://www.geeksforgeeks.org/deep-learning/artificial-neural-networks-and-its-applications/) - [Recurrent Neural Networks (RNNs)](https://www.geeksforgeeks.org/machine-learning/introduction-to-recurrent-neural-network/) - [Long Short-Term Memory (LSTM)](https://www.geeksforgeeks.org/deep-learning/deep-learning-introduction-to-long-short-term-memory/) - [Gated Recurrent Unit (GRU)](https://www.geeksforgeeks.org/machine-learning/gated-recurrent-unit-networks/) - [Seq2Seq Models](https://www.geeksforgeeks.org/machine-learning/seq2seq-model-in-machine-learning/) - [Transformer Models](https://www.geeksforgeeks.org/machine-learning/getting-started-with-transformers/) ### Pre-Trained Language Models - [GPT (Generative Pre-trained Transformer)](https://www.geeksforgeeks.org/artificial-intelligence/introduction-to-generative-pre-trained-transformer-gpt/) - [Transformers XL](https://www.geeksforgeeks.org/nlp/trasformer-xl-beyond-a-fixed-length-context/) - [T5 (Text-to-Text Transfer Transformer)](https://www.geeksforgeeks.org/nlp/t5-text-to-text-transfer-transformer/) - [Transfer Learning with Fine-tuning](https://www.geeksforgeeks.org/nlp/transfer-learning-and-fine-tuning-in-nlp/) ## NLP Tasks Core NLP tasks that help machines understand, interpret and generate human language. - Text Classification: [Dataset for Text Classification](https://www.geeksforgeeks.org/nlp/dataset-for-text-classification/), [Naive Bayes](https://www.geeksforgeeks.org/machine-learning/classification-of-text-documents-using-the-approach-of-naive-bayes/), [Logistic Regression](https://www.geeksforgeeks.org/machine-learning/text-classification-using-logistic-regression/), [RNNs](https://www.geeksforgeeks.org/nlp/rnn-for-text-classifications-in-nlp/), [CNNs](https://www.geeksforgeeks.org/nlp/text-classification-using-cnn/) - Information Extraction [Named Entity Recognition (NER)](https://www.geeksforgeeks.org/python/python-named-entity-recognition-ner-using-spacy/), [NLTK,](https://www.geeksforgeeks.org/nlp/named-entity-recognition/) [Relationship Extraction](https://www.geeksforgeeks.org/nlp/relationship-extraction-in-nlp/), [Word Sense Disambiguation (WSD)](https://www.geeksforgeeks.org/machine-learning/word-sense-disambiguation-in-natural-language-processing/) - Sentiment Analysis: [VADER,](https://www.geeksforgeeks.org/python/python-sentiment-analysis-using-vader/) [RNN](https://www.geeksforgeeks.org/python/sentiment-analysis-with-an-recurrent-neural-networks-rnn/), [Opinion Mining](https://www.geeksforgeeks.org/nlp/opinion-mining-in-nlp/) - Machine Translation: [Statistical Machine Translation of Language](https://www.geeksforgeeks.org/artificial-intelligence/statistical-machine-translation-of-languages-in-artificial-intelligence/), [Machine Translation with Transformer](https://www.geeksforgeeks.org/nlp/machine-translation-with-transformer-in-python/) - Text Summarization: [Hugging Face Model](https://www.geeksforgeeks.org/nlp/text-summarizations-using-huggingface-model/), [Sumy](https://www.geeksforgeeks.org/nlp/mastering-text-summarization-with-sumy-a-python-library-overview/) - Text Generation: [Fnet](https://www.geeksforgeeks.org/nlp/text-generation-using-fnet/), [LSTM](https://www.geeksforgeeks.org/machine-learning/text-generation-using-recurrent-long-short-term-memory-network/), [HuggingFace Model](https://www.geeksforgeeks.org/nlp/text2text-generations-using-huggingface-model/) - Question Answering: [Retrieval-Based QA](https://www.geeksforgeeks.org/nlp/what-is-retrieval-augmented-generation-rag/), [Generative QA](https://www.geeksforgeeks.org/artificial-intelligence/data-management-in-generative-ai/) ## Applications - Voice Assistants: Alexa, Siri and Google Assistant use NLP for voice recognition and interaction. - Grammar and Text Analysis: Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking. - Information Extraction: Search engines like Google and DuckDuckGo use NLP to extract relevant information. - Chatbots:** Website bots and customer support chatbots leverage NLP for automated conversations. > For more details you can refer to: [Applications of NLP](https://www.geeksforgeeks.org/nlp/top-7-applications-of-natural-language-processing/)
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