What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is the field of artificial intelligence focused on enabling computers to understand, interpret, generate, and reason about human language — in both text and speech form.
Natural Language Processing (NLP): Full Explanation
Natural Language Processing (NLP) is one of the oldest and most practically impactful areas of AI. For decades before LLMs existed, NLP powered email spam filters, sentiment analysis tools, machine translation, and speech recognition. Today, NLP is the foundation on which LLMs are built — and the two terms are increasingly used together.
NLP encompasses a wide range of tasks: Named Entity Recognition (identifying people, organisations, and places in text), Sentiment Analysis (determining whether text expresses positive, negative, or neutral sentiment), Text Classification, Machine Translation, Summarisation, Question Answering, and Intent Detection.
The LLM revolution has transformed NLP: tasks that previously required custom model training for each specific task can now be performed by a single general-purpose LLM with appropriate prompting. But specialised NLP models still outperform LLMs in narrow, high-volume tasks where inference speed and cost matter.
Key Facts About Natural Language Processing (NLP)
- ✓NLP is the broader field; LLMs are the most powerful recent class of NLP models.
- ✓Common NLP tasks: sentiment analysis, named entity recognition, classification, summarisation, translation, and intent detection.
- ✓NLP is already embedded in products you use: Google Search, Gmail auto-reply, Alexa/Siri, and customer service chatbots.
- ✓Transformer architecture (2017) was the breakthrough that enabled both modern NLP models and LLMs.
- ✓Specialised NLP models still outperform general LLMs on narrow, high-volume tasks where speed and cost matter.
- ✓India-specific NLP challenges include multilingual support (22 official languages), code-switching (Hindi-English mixing), and transliteration.
Real-World Example: Banking & Financial Services
A private bank deployed NLP models across two processes: (1) Sentiment analysis on customer service call transcripts, flagging dissatisfied customers within 30 minutes for proactive outreach — reducing escalation rates by 28%. (2) Regulatory document parsing — extracting obligation dates and compliance thresholds from RBI circulars automatically, reducing compliance analyst review time by 60%.
Frequently Asked Questions
What is the difference between NLP and LLMs?
NLP is the broad field covering all techniques for processing human language with computers. LLMs are a specific, very powerful class of NLP models. All LLMs do NLP, but NLP includes many other approaches (rule-based systems, classical ML models) that predate and complement LLMs.
Is sentiment analysis still relevant in the age of ChatGPT?
Yes. Specialised sentiment models trained on domain-specific data (product reviews, financial news, healthcare records) still outperform general LLMs for high-volume, narrow tasks in terms of accuracy, cost, and latency. LLMs are better for nuanced, context-rich sentiment interpretation.
Does NLP work for Indian languages?
Support for Indian languages in NLP has improved significantly. Google Translate, Azure Cognitive Services, and AWS Comprehend cover most major Indian languages. Multilingual LLMs like mBERT, IndicBERT, and recent GPT/Claude versions have reasonable but not perfect performance in Hindi, Tamil, Telugu, and other Indian languages.