Natural language processing: Difference between revisions

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{{About|computer processing|human brain processing|Language processing in the brain}}

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'''Natural language processing''' ('''NLP''') is a subfield of [[computer science]] and especially [[artificial intelligence]]. It is primarily concerned with providing computers with the ability to process data encoded in [[natural language]] and is thus closely related to [[information retrieval]], [[knowledge representation]] and [[computational linguistics]], a subfield of [[linguistics]].

=== 1. What is NLP? ===

Major tasks in natural language processing are [[speech recognition]], [[text classification]], [[natural-language understanding]], and [[natural language generation|natural-language generation]].

Natural Language Processing (NLP) is a specialized branch of '''computer science and artificial intelligence'''. Its main goal is to enable computers to understand, interpret, and generate human language, just like people do. Instead of relying on programming languages or code, NLP focuses on processing '''natural languages'''—like English, Hindi, or Telugu—that humans use every day.

=== 2. Purpose of NLP ===

The core idea behind NLP is to make communication between humans and machines smoother and more natural. This involves teaching machines how to read, listen to, and even respond to text or speech in ways that are meaningful. It connects deeply with areas such as '''information retrieval''' (like search engines), '''knowledge representation''' (storing meaning and facts), and '''computational linguistics''' (applying linguistics through computers).

=== 3. Key Goals and Challenges ===

NLP is not just about translating words; it’s about understanding '''context, grammar, and meaning'''. For example, the word “bank” can mean a financial institution or the edge of a river. NLP systems must learn to figure out which meaning fits based on context. This makes the task both powerful and challenging—because human language is full of '''ambiguity, slang, and emotion'''.

=== 4. Major Tasks in NLP ===

There are several major tasks in NLP that help achieve these goals:

* '''Speech Recognition''': Converting spoken words into text (used in voice assistants like Siri).

* '''Text Classification''': Grouping or tagging text into categories (like spam vs. non-spam emails).

* '''Natural Language Understanding (NLU)''': Helping machines understand the meaning behind words and phrases.

* '''Natural Language Generation (NLG)''': Enabling machines to produce text or speech that sounds natural.

=== 5. Applications in the Real World ===

NLP is used in everyday technology—like Google Translate, Alexa, Grammarly, and chatbots on websites. It helps in sentiment analysis (e.g., analyzing customer reviews), document summarization, and even in healthcare to process medical records. Its applications are growing rapidly in fields such as education, law, marketing, and customer service.

=== 6. The Future of NLP ===

With the rise of '''machine learning and deep learning''', NLP is becoming even more powerful. Models like ChatGPT and BERT can understand and generate human-like responses. As research continues, NLP is expected to play a bigger role in making AI systems more intelligent, ethical, and responsive to human needs.

== History ==