Learn the basics and advanced concepts of natural language processing (NLP) with our complete NLP tutorial and get ready to explore the vast and exciting field of NLP, where technology meets human language.
NLP tutorial is designed for both beginners and professionals. Whether you’re a data scientist, a developer, or someone curious about the power of language, our tutorial will provide you with the knowledge and skills you need to take your understanding of NLP to the next level.
What is NLP?
NLP stands for Natural Language Processing. It is the branch of Artificial Intelligence that gives the ability to machine understand and process human languages. Human languages can be in the form of text or audio format.
History of NLP
Natural Language Processing started in 1950 When Alan Mathison Turing published an article in the name Computing Machinery and Intelligence. It is based on Artificial intelligence. It talks about automatic interpretation and generation of natural language. As the technology evolved, different approaches have come to deal with NLP tasks.
- Heuristics-Based NLP: This is the initial approach of NLP. It is based on defined rules. Which comes from domain knowledge and expertise. Example: regex
- Statistical Machine learning-based NLP: It is based on statistical rules and machine learning algorithms. In this approach, algorithms are applied to the data and learned from the data, and applied to various tasks. Examples: Naive Bayes, support vector machine (SVM), hidden Markov model (HMM), etc.
- Neural Network-based NLP: This is the latest approach that comes with the evaluation of neural network-based learning, known as Deep learning. It provides good accuracy, but it is a very data-hungry and time-consuming approach. It requires high computational power to train the model. Furthermore, it is based on neural network architecture. Examples: Recurrent neural networks (RNNs), Long short-term memory networks (LSTMs), Convolutional neural networks (CNNs), Transformers, etc.
Advantages of NLP
- NLP helps us to analyse data from both structured and unstructured sources.
- NLP is very fast and time efficient.
- NLP offers end-to-end exact answers to the question. So, It saves time that going to consume unnecessary and unwanted information.
- NLP offers users to ask questions about any subject and give a direct response within milliseconds.
Disadvantages of NLP
- For the training of the NLP model, A lot of data and computation are required.
- Many issues arise for NLP when dealing with informal expressions, idioms, and cultural jargon.
- NLP results are sometimes not to be accurate, and accuracy is directly proportional to the accuracy of data.
- NLP is designed for a single, narrow job since it cannot adapt to new domains and has a limited function.
Components of NLP
There are two components of Natural Language Processing:
- Natural Language Understanding
- Natural Language Generation
Applications of NLP
The applications of Natural Language Processing are as follows:
- Text and speech processing like-Voice assistants – Alexa, Siri, etc.
- Text classification like Grammarly, Microsoft Word, and Google Docs
- Information extraction like-Search engines like DuckDuckGo, Google
- Chatbot and Question Answering like:- website bots
- Language Translation like:- Google Translate
- Text summarization
Phases of Natural Language Processing
Classical Approaches to Natural Language Processing
- Text Preprocessing
- Text Vectorization or Encoding:
- vector space model (VSM)
- Words and vectors
- Cosine similarity
- Basic Text Vectorization approach:
- Distributed Representations:
- Universal Text Representations
- Embeddings Visualizations
- t-sne (t-distributed Stochastic Neighbouring Embedding)
- Embeddings semantic properties
- Semantic Analysis
- Parts of Speech tagging and Named Entity Recognizations:
- Neural Network for NLP:
- Transfer Learning for NLP:
- Informations Extractions
- Keyphrase Extraction
- Named Entity Recognition
- Relationship Extraction
- Information Retrieval
- Text Generations
- Text summarization
- Questions – Answering
- Chatbot & Dialogue Systems:
- Machine translation
- Speech Recognition and Text-to-Speech
Empirical and Statistical Approaches
- Treebank Annotation
- Fundamental Statistical Techniques for NLP
- Part-of-Speech Tagging
- Rules-based system
- Statistical Parsing
- Multiword Expressions
- Normalized Web Distance and Word Similarity
- Word Sense Disambiguation
FAQs on Natural Language Processing
What is the most difficult part of natural language processing?
Ambiguity is the main challenge of natural language processing because in natural language, words are unique, but they have different meanings depending upon the context which causes ambiguity on lexical, syntactic, and semantic levels.
What are the 4 pillars of NLP?
The four main pillars of NLP are 1.) Outcomes, 2.) Sensory acuity, 3.) behavioural flexibility, and 4.) report.
What language is best for natural language processing?
Python is considered the best programming language for NLP because of their numerous libraries, simple syntax, and ability to easily integrate with other programming languages.
What is the life cycle of NLP?
There are four stages included in the life cycle of NLP – development, validation, deployment, and monitoring of the models.
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