NLP (Natural Language Processing)

Natural language processing (NLP) covers techniques that let software work with human language. Tasks include translation, sentiment analysis, named-entity recognition, summarization, question answering, and speech recognition.

Why it matters

NLP turns unstructured text, which is most of the world's data, into something searchable and measurable. It drives search relevance, support ticket routing, spam detection, and today's large language models. Language is ambiguous, so results should be tested on text that resembles your real inputs.

Example

Sentiment analysis labels a review:

"Checkout kept timing out, very frustrating"
-> {"sentiment": "negative", "score": 0.93}

Named-entity recognition would pull out items like company names, dates, or locations from the same kind of text.

Common mistakes

• Expecting English-trained models to work equally well in other languages or dialects.
• Overlooking sarcasm, slang, and domain jargon that confuse sentiment models.
• Skipping text data cleaning, such as removing boilerplate or duplicates.

Related

• Large language model
• Token
• Embedding
• Unstructured data

यह पेज सामान्य जानकारी है, जिसे हमारी संपादकीय टीम ने लिखा है और दस्तावेज़ीकरण बदलने पर अपडेट किया जाता है। सर्वर और प्रदाता अलग-अलग होते हैं, इसलिए बदलाव पहले सुरक्षित तरीके से जाँचें। देखें: संपादकीय नीति और अस्वीकरण.