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Natural Language Processing (NLP) Tutorial

Last Updated : 23 Jul, 2025

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 Understanding
• Natural Language Generation

It involves a series of phases that work together to process and interpret language with each phase contributing to understanding its structure and meaning.

For more details you can refer to: Phases of NLP

Some of natural language processing libraries include:

• NLTK (Natural Language Toolkit)
• spaCy
• TextBlob
• Transformers (by Hugging Face)
• Gensim
• NLP Libraries in Python.

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 Normalization
• Regular Expressions (RE)
• How to write Regular Expressions?
• Properties of Regular Expressions
• Email Extraction using RE

2. Tokenization is a process of splitting text into smaller units called tokens.

• Tokenization
• Word Tokenization
• Rule-based Tokenization
• Subword Tokenization
• Dictionary-Based Tokenization
• Whitespace Tokenization
• WordPiece 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:

• Stemming
• Porter Stemmer
• Lancaster Stemmer
• Snowball Stemmer
• Rule-based Stemming

5. 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) Tagging

Lets see how these techniques works in NLP.

It converts textual data into numerical vectors that are processed by the following methods:

• One-Hot Encoding
• Bag of Words (BOW)
• Term Frequency-Inverse Document Frequency (TF-IDF)
• N-Gram Language Modeling with NLTK
• Latent Semantic Analysis (LSA)
• Latent Dirichlet Allocation (LDA)

It refers to methods that create dense vector representations of text, capturing semantic meaning including advanced approaches like:

1. Word Embedding

• Word2Vec (SkipGram, Continuous Bag of Words - CBOW)
• GloVe (Global Vectors for Word Representation)
• fastText

2. Pre-Trained Embedding

• ELMo (Embeddings from Language Models)
• BERT (Bidirectional Encoder Representations from Transformers)

3. Document Embedding

• Doc2Vec

4. Advanced Embeddings

• RoBERTa
• DistilBERT

Deep 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 learning
• Artificial Neural Networks (ANNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)
• Seq2Seq Models
• Transformer Models

Pre-trained models can be fine-tuned for specific tasks:

• Pre-trained models
• GPT (Generative Pre-trained Transformer)
• Transformers XL
• T5 (Text-to-Text Transfer Transformer)
• Transfer Learning with Fine-tuning

Core NLP tasks that help machines understand, interpret and generate human language.

• Dataset for Text Classification
• Text Classification using Naive Bayes
• Text Classification using Logistic Regression
• Text Classification using RNNs
• Text Classification using CNNs

• Named Entity Recognition (NER) using SpaCy
• Named Entity Recognition (NER) using NLTK
• Relationship Extraction

• What is Sentiment Analysis?
• Sentiment Analysis using VADER
• Sentiment Analysis using Recurrent Neural Networks (RNN)

• Statistical Machine Translation of Language
• Machine Translation with Transformer

• What is Text Summarization?
• Text Summarizations using Hugging Face Model
• Text Summarization using Sumy

• Text Generation using Fnet
• Text Generation using Recurrent Long Short Term Memory Network
• Text2Text Generations using HuggingFace Model

NLP 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?

1. Voice Assistants: Alexa, Siri and Google Assistant use NLP for voice recognition and interaction.
2. Grammar and Text Analysis: Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking.
3. Information Extraction: Search engines like Google and DuckDuckGo use NLP to extract relevant information.
4. Chatbots: Website bots and customer support chatbots leverage NLP for automated conversations.

For more details you can refer to: Applications of NLP

Natural 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:

1. Information Extraction: Extracts useful data from unstructured content.
2. Sentiment Analysis: Analyzes customer opinions for businesses.
3. Automation: Streamlines tasks like customer service and document processing.
4. Language Translation: Breaks down language barriers with tools like Google Translate.
5. Healthcare: Assists in analyzing medical records and research.

For more details you can refer to: Why is NLP important?

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