Natural Language Processing (NLP) Tutorial Last Updated : 26 Jun, 2025 Summarize Comments Improve Suggest changes Share Like Article Like Report Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that 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.Natural Language Processing can be categorized into two components:1. Natural Language Understanding: It involves interpreting the meaning of the text.2. Natural Language Generation: It involves generating human-like text based on processed data.Natural Language UnderstandingNatural Language GenerationPhases of Natural Language ProcessingIt involves a series of phases that work together to process and interpret language with each phase contributing to understanding its structure and meaning.Phases of NLPFor more details you can refer to: Phases of NLPLibraries for NLPSome of natural language processing libraries include: NLTK (Natural Language Toolkit)spaCyTextBlobTransformers (by Hugging Face)GensimNLP Libraries in Python.Normalizing Textual Data in NLP Text Normalization transforms text into a consistent format improves the quality and makes it easier to process in NLP tasks. Key steps in text normalization includes: 1. Regular Expressions (RE) are sequences of characters that define search patterns. Text NormalizationRegular Expressions (RE) How to write Regular Expressions?Properties of Regular ExpressionsEmail Extraction using RE2. Tokenization is a process of splitting text into smaller units called tokens. TokenizationWord TokenizationRule-based TokenizationSubword TokenizationDictionary-Based TokenizationWhitespace TokenizationWordPiece Tokenization 3. Lemmatization reduces words to their base or root form.Lemmatization 4. Stemming reduces works to their root by removing suffixes. Types of stemmers include:StemmingPorter StemmerLancaster Stemmer Snowball Stemmer Rule-based Stemming5. Stopword removal is a process to remove common words from the document. Stopword removal 6. Parts of Speech (POS) Tagging assigns a part of speech to each word in sentence based on definition and context. Parts of Speech (POS) TaggingText Representation and Embedding Techniques in NLP Lets see how these techniques works in NLP.Text representation TechniquesIt converts textual data into numerical vectors that are processed by the following methods:One-Hot EncodingBag of Words (BOW)Term Frequency-Inverse Document Frequency (TF-IDF)N-Gram Language Modeling with NLTKLatent Semantic Analysis (LSA)Latent Dirichlet Allocation (LDA)Text Embedding TechniquesIt refers to methods that create dense vector representations of text, capturing semantic meaning including advanced approaches like:1. Word EmbeddingWord2Vec (SkipGram, Continuous Bag of Words - CBOW)GloVe (Global Vectors for Word Representation)fastText2. Pre-Trained EmbeddingELMo (Embeddings from Language Models)BERT (Bidirectional Encoder Representations from Transformers)3. Document Embedding Doc2Vec4. Advanced EmbeddingsRoBERTaDistilBERTDeep Learning Techniques for NLPDeep learning has revolutionized Natural Language Processing by helping models to automatically learn complex patterns from raw text. Key deep learning techniques in NLP include:Deep learningArtificial Neural Networks (ANNs)Recurrent Neural Networks (RNNs)Long Short-Term Memory (LSTM)Gated Recurrent Unit (GRU)Seq2Seq ModelsTransformer ModelsPre-Trained Language Models Pre-trained models can be fine-tuned for specific tasks:Pre-trained modelsGPT (Generative Pre-trained Transformer)Transformers XLT5 (Text-to-Text Transfer Transformer)Transfer Learning with Fine-tuningNatural Language Processing Tasks Core NLP tasks that help machines understand, interpret and generate human language.1. Text Classification Dataset for Text Classification Text Classification using Naive Bayes Text Classification using Logistic Regression Text Classification using RNNs Text Classification using CNNs 2. Information Extraction Named Entity Recognition (NER) using SpaCy Named Entity Recognition (NER) using NLTK Relationship Extraction3. Sentiment AnalysisWhat is Sentiment Analysis?Sentiment Analysis using VADER Sentiment Analysis using Recurrent Neural Networks (RNN)4. Machine TranslationStatistical Machine Translation of Language Machine Translation with Transformer5. Text SummarizationWhat is Text Summarization?Text Summarizations using Hugging Face Model Text Summarization using Sumy 6. Text GenerationText Generation using FnetText Generation using Recurrent Long Short Term Memory NetworkText2Text Generations using HuggingFace ModelNatural Language Processing ChatbotsNLP chatbots are computer programs designed to interact with users in natural language helps in seamless communication between humans and machines. By using NLP techniques, these chatbots understand, interpret and generate human language.What is Natural Language Processing (NLP) Chatbots?Applications of NLPVoice 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 NLPImportance of NLPNatural Language Processing (NLP) plays an important role in transforming how we interact with technology and understand data. Below are reasons why it’s so important:Information Extraction: Extracts useful data from unstructured content.Sentiment Analysis: Analyzes customer opinions for businesses.Automation: Streamlines tasks like customer service and document processing.Language Translation: Breaks down language barriers with tools like Google Translate.Healthcare: Assists in analyzing medical records and research.For more details you can refer to: Why is NLP important? Comment More infoAdvertise with us Next Article Natural Language Processing (NLP) - Overview A abhishek1 Follow Improve Article Tags : NLP Natural-language-processing python Practice Tags : python Similar Reads Natural Language Processing (NLP) Tutorial Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that 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.Natural Languag 5 min read Introduction to NLPNatural Language Processing (NLP) - OverviewNatural Language Processing (NLP) is a field that combines computer science, artificial intelligence and language studies. It helps computers understand, process and create human language in a way that makes sense and is useful. With the growing amount of text data from social media, websites and ot 9 min read NLP vs NLU vs NLGNatural Language Processing(NLP) is a subset of Artificial intelligence which involves communication between a human and a machine using a natural language than a coded or byte language. It provides the ability to give instructions to machines in a more easy and efficient manner. Natural Language Un 3 min read Applications of NLPAmong the thousands and thousands of species in this world, solely homo sapiens are successful in spoken language. From cave drawings to internet communication, we have come a lengthy way! As we are progressing in the direction of Artificial Intelligence, it only appears logical to impart the bots t 6 min read Why is NLP important?Natural language processing (NLP) is vital in efficiently and comprehensively analyzing text and speech data. It can navigate the variations in dialects, slang, and grammatical inconsistencies typical of everyday conversations. Table of Content Understanding Natural Language ProcessingReasons Why NL 6 min read Phases of Natural Language Processing (NLP)Natural Language Processing (NLP) helps computers to understand, analyze and interact with human language. It involves a series of phases that work together to process language and each phase helps in understanding structure and meaning of human language. In this article, we will understand these ph 7 min read The Future of Natural Language Processing: Trends and InnovationsThere are no reasons why today's world is thrilled to see innovations like ChatGPT and GPT/ NLP(Natural Language Processing) deployments, which is known as the defining moment of the history of technology where we can finally create a machine that can mimic human reaction. If someone would have told 7 min read Libraries for NLPNLTK - NLPNatural Language Toolkit (NLTK) is one of the largest Python libraries for performing various Natural Language Processing tasks. From rudimentary tasks such as text pre-processing to tasks like vectorized representation of text - NLTK's API has covered everything. In this article, we will accustom o 5 min read Tokenization Using SpacyBefore we get into tokenization, let's first take a look at what spaCy is. spaCy is a popular library used in Natural Language Processing (NLP). It's an object-oriented library that helps with processing and analyzing text. We can use spaCy to clean and prepare text, break it into sentences and word 3 min read Python | Tokenize text using TextBlobTokenization is a fundamental task in Natural Language Processing that breaks down a text into smaller units such as words or sentences which is used in tasks like text classification, sentiment analysis and named entity recognition. TextBlob is a python library for processing textual data and simpl 3 min read Hugging Face Transformers IntroductionHugging Face is an online community where people can team up, explore, and work together on machine-learning projects. Hugging Face Hub is a cool place with over 350,000 models, 75,000 datasets, and 150,000 demo apps, all free and open to everyone. In this article we are going to understand a brief 10 min read NLP Gensim Tutorial - Complete Guide For BeginnersThis tutorial is going to provide you with a walk-through of the Gensim library.Gensim : It is an open source library in python written by Radim Rehurek which is used in unsupervised topic modelling and natural language processing. It is designed to extract semantic topics from documents. It can han 14 min read NLP Libraries in PythonIn today's AI-driven world, text analysis is fundamental for extracting valuable insights from massive volumes of textual data. Whether analyzing customer feedback, understanding social media sentiments, or extracting knowledge from articles, text analysis Python libraries are indispensable for data 15+ min read Text Normalization in NLPNormalizing Textual Data with PythonIn this article, we will learn How to Normalizing Textual Data with Python. Let's discuss some concepts : Textual data ask systematically collected material consisting of written, printed, or electronically published words, typically either purposefully written or transcribed from speech.Text normal 7 min read Regex Tutorial - How to write Regular Expressions?A regular expression (regex) is a sequence of characters that define a search pattern. Here's how to write regular expressions: Start by understanding the special characters used in regex, such as ".", "*", "+", "?", and more.Choose a programming language or tool that supports regex, such as Python, 6 min read Tokenization in NLPTokenization is a fundamental step in Natural Language Processing (NLP). It involves dividing a Textual input into smaller units known as tokens. These tokens can be in the form of words, characters, sub-words, or sentences. It helps in improving interpretability of text by different models. Let's u 8 min read Python | Lemmatization with NLTKLemmatization is a fundamental text pre-processing technique widely applied in natural language processing (NLP) and machine learning. Serving a purpose akin to stemming, lemmatization seeks to distill words to their foundational forms. In this linguistic refinement, the resultant base word is refer 6 min read Introduction to StemmingStemming is a method in text processing that eliminates prefixes and suffixes from words, transforming them into their fundamental or root form, The main objective of stemming is to streamline and standardize words, enhancing the effectiveness of the natural language processing tasks. The article ex 8 min read Removing stop words with NLTK in PythonIn natural language processing (NLP), stopwords are frequently filtered out to enhance text analysis and computational efficiency. Eliminating stopwords can improve the accuracy and relevance of NLP tasks by drawing attention to the more important words, or content words. The article aims to explore 9 min read POS(Parts-Of-Speech) Tagging in NLPOne of the core tasks in Natural Language Processing (NLP) is Parts of Speech (PoS) tagging, which is giving each word in a text a grammatical category, such as nouns, verbs, adjectives, and adverbs. Through improved comprehension of phrase structure and semantics, this technique makes it possible f 11 min read Text Representation and Embedding TechniquesOne-Hot Encoding in NLPNatural Language Processing (NLP) is a quickly expanding discipline that works with computer-human language exchanges. One of the most basic jobs in NLP is to represent text data numerically so that machine learning algorithms can comprehend it. One common method for accomplishing this is one-hot en 9 min read Bag of words (BoW) model in NLPIn this article, we are going to discuss a Natural Language Processing technique of text modeling known as Bag of Words model. Whenever we apply any algorithm in NLP, it works on numbers. We cannot directly feed our text into that algorithm. Hence, Bag of Words model is used to preprocess the text b 4 min read Understanding TF-IDF (Term Frequency-Inverse Document Frequency)TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used in natural language processing and information retrieval to evaluate the importance of a word in a document relative to a collection of documents (corpus). Unlike simple word frequency, TF-IDF balances common and rare w 6 min read N-Gram Language Modelling with NLTKLanguage modeling is the way of determining the probability of any sequence of words. Language modeling is used in various applications such as Speech Recognition, Spam filtering, etc. Language modeling is the key aim behind implementing many state-of-the-art Natural Language Processing models.Metho 5 min read Word Embedding using Word2VecWord Embedding is a language modelling technique that maps words to vectors (numbers). It represents words or phrases in vector space with several dimensions. Various methods such as neural networks, co-occurrence matrices and probabilistic models can generate word embeddings.. Word2Vec is also a me 6 min read Pre-trained Word embedding using Glove in NLP modelsIn modern Natural Language Processing (NLP), understanding and processing human language in a machine-readable format is essential. Since machines interpret numbers, it's important to convert textual data into numerical form. One of the most effective and widely used approaches to achieve this is th 7 min read Overview of Word Embedding using Embeddings from Language Models (ELMo)What is word embeddings? It is the representation of words into vectors. These vectors capture important information about the words such that the words sharing the same neighborhood in the vector space represent similar meaning. There are various methods for creating word embeddings, for example, W 2 min read NLP Deep Learning TechniquesNLP with Deep LearningNatural Language Processing (NLP) is a subfield of AI focused on making machines to understand, interpret, generate and respond to human language. Deep Learning (DL) involves training neural networks to extract hierarchical features from data. NLP using Deep Learning integrates DL models to better c 3 min read Introduction to Recurrent Neural NetworksRecurrent Neural Networks (RNNs) differ from regular neural networks in how they process information. While standard neural networks pass information in one direction i.e from input to output, RNNs feed information back into the network at each step.Lets understand RNN with a example:Imagine reading 10 min read What is LSTM - Long Short Term Memory?Long Short-Term Memory (LSTM) is an enhanced version of the Recurrent Neural Network (RNN) designed by Hochreiter and Schmidhuber. LSTMs can capture long-term dependencies in sequential data making them ideal for tasks like language translation, speech recognition and time series forecasting. Unlike 5 min read Gated Recurrent Unit NetworksIn machine learning Recurrent Neural Networks (RNNs) are essential for tasks involving sequential data such as text, speech and time-series analysis. While traditional RNNs struggle with capturing long-term dependencies due to the vanishing gradient problem architectures like Long Short-Term Memory 6 min read Transformers in Machine LearningTransformer is a neural network architecture used for performing machine learning tasks particularly in natural language processing (NLP) and computer vision. In 2017 Vaswani et al. published a paper " Attention is All You Need" in which the transformers architecture was introduced. The article expl 4 min read seq2seq ModelThe Sequence-to-Sequence (Seq2Seq) model is a type of neural network architecture widely used in machine learning particularly in tasks that involve translating one sequence of data into another. It takes an input sequence, processes it and generates an output sequence. The Seq2Seq model has made si 4 min read Top 5 PreTrained Models in Natural Language Processing (NLP)Pretrained models are deep learning models that have been trained on huge amounts of data before fine-tuning for a specific task. The pre-trained models have revolutionized the landscape of natural language processing as they allow the developer to transfer the learned knowledge to specific tasks, e 7 min read NLP Projects and PracticeSentiment Analysis with an Recurrent Neural Networks (RNN)Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews 5 min read Text Generation using Recurrent Long Short Term Memory NetworkLSTMs are a type of neural network that are well-suited for tasks involving sequential data such as text generation. They are particularly useful because they can remember long-term dependencies in the data which is crucial when dealing with text that often has context that spans over multiple words 4 min read Machine Translation with Transformer in PythonMachine translation means converting text from one language into another. Tools like Google Translate use this technology. Many translation systems use transformer models which are good at understanding the meaning of sentences. In this article, we will see how to fine-tune a Transformer model from 6 min read Building a Rule-Based Chatbot with Natural Language ProcessingA rule-based chatbot follows a set of predefined rules or patterns to match user input and generate an appropriate response. The chatbot can’t understand or process input beyond these rules and relies on exact matches making it ideal for handling repetitive tasks or specific queries.Pattern Matching 4 min read Text Classification using scikit-learn in NLPThe purpose of text classification, a key task in natural language processing (NLP), is to categorise text content into preset groups. Topic categorization, sentiment analysis, and spam detection can all benefit from this. In this article, we will use scikit-learn, a Python machine learning toolkit, 5 min read Text Summarizations using HuggingFace ModelText summarization is a crucial task in natural language processing (NLP) that involves generating concise and coherent summaries from longer text documents. This task has numerous applications, such as creating summaries for news articles, research papers, and long-form content, making it easier fo 5 min read Advanced Natural Language Processing Interview QuestionNatural Language Processing (NLP) is a rapidly evolving field at the intersection of computer science and linguistics. As companies increasingly leverage NLP technologies, the demand for skilled professionals in this area has surged. Whether preparing for a job interview or looking to brush up on yo 9 min read Like