Natural Language Processing (NLP) Tutorial

Last Updated : 17 Dec, 2024

Natural Language Processing (NLP) is the branch of Artificial Intelligence (AI) that gives the ability to machine understand and process human languages. Human languages can be in the form of text or audio format.

Applications of NLP

The applications of Natural Language Processing are as follows:

Voice Assistants like Alexa, Siri, and Google Assistant use NLP for voice recognition and interaction.
Tools like Grammarly, Microsoft Word, and Google Docs apply NLP for grammar checking and text analysis.
Information extraction through Search engines such as Google and DuckDuckGo.
Website bots and customer support chatbots leverage NLP for automated conversations and query handling.
Google Translate and similar services use NLP for real-time translation between languages.
Text summarization

This NLP tutorial is designed for both beginners and professionals. Whether you are a beginner or a data scientist, this guide will provide you with the knowledge and skills you need to take your understanding of NLP to the next level.

Phases of Natural Language Processing

There are two components of Natural Language Processing:

Natural Language Understanding
Natural Language Generation

Libraries for Natural Language Processing

Some of natural language processing libraries include:

To explore in detail, you can refer to this article: NLP 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.

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

3. Lemmatization reduces words to their base or root form.

4. Stemming reduces works to their root by removing suffixes. Types of stemmers include:

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

5. Stopword removal is a process to remove common words from the document.

6. Parts of Speech (POS) Tagging assigns a part of speech to each word in sentence based on definition and context.

Text Representation or Text Embedding Techniques in NLP

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

Text Embedding Techniques refer to the methods and models used to create these vector representations, including traditional methods (like TFIDF and BOW) and more advanced approaches:

1. Word Embedding

2. Pre-Trained Embedding

3. Document Embedding - Doc2Vec

Deep Learning Techniques for NLP

Deep learning has revolutionized Natural Language Processing (NLP) by enabling models to automatically learn complex patterns and representations from raw text. Below are some of the key deep learning techniques used in NLP:

Pre-Trained Language Models

Pre-trained models understand language patterns, context and semantics. The provided models are trained on massive corpora and can be fine tuned for specific tasks.

To learn how to fine tune a model, refer to this article: Transfer Learning with Fine-tuning

Natural Language Processing Tasks

1. Text Classification

2. Information Extraction

3. Sentiment Analysis

4. Machine Translation

5. Text Summarization

6. Text Generation

History of NLP

Natural Language Processing (NLP) emerged in 1950 when Alan Turing published his groundbreaking paper titled Computing Machinery and Intelligence. Turing’s work laid the foundation for NLP, which is a subset of Artificial Intelligence (AI) focused on enabling machines to automatically interpret and generate human language. Over time, NLP technology has evolved, giving rise to different approaches for solving complex language-related tasks.

1. Heuristic-Based NLP

The Heuristic-based approach to NLP was one of the earliest methods used in natural language processing. It relies on predefined rules and domain-specific knowledge. These rules are typically derived from expert insights. A classic example of this approach is Regular Expressions (Regex), which are used for pattern matching and text manipulation tasks.

2. Statistical and Machine Learning-Based NLP

As NLP advanced, Statistical NLP emerged, incorporating machine learning algorithms to model language patterns. This approach applies statistical rules and learns from data to tackle various language processing tasks. Popular machine learning algorithms in this category include:

3. Neural Network-Based NLP (Deep Learning)

The most recent advancement in NLP is the adoption of Deep Learning techniques. Neural networks, particularly Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Transformers, have revolutionized NLP tasks by providing superior accuracy. These models require large amounts of data and considerable computational power for training

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