NLP definition
Natural language processing (NLP) is the branch of artificial intelligence that enables computers to read, understand and generate human language. NLP systems turn text or speech into structured data or useful output, handling tasks such as translation, sentiment analysis, entity extraction, summarization, search and chatbots. Modern NLP is built largely on transformer-based language models.
How does natural language processing work?
Text first has to become numbers. A tokenizer splits it into words or subword pieces, and each piece is mapped to an embedding, a vector that captures meaning learned from large amounts of text. A model, today usually a transformer, reads those vectors with attention so each word is interpreted in context: "bank" in "river bank" and "bank loan" ends up with different representations. An output layer then produces a label, an extracted span or generated text.
Older NLP relied on hand-built pipelines: tokenization, part-of-speech tagging, parsing and rules written by linguists, or statistical models over word counts such as TF-IDF. Those methods still work for simple, high-volume tasks like keyword routing, and they are cheap and predictable. Most new projects, however, start from a pretrained language model and adapt it with fine-tuning or prompting.
Common NLP tasks
Most production systems chain several tasks. A contract review tool might run OCR, then entity recognition to find parties and dates, then classification to flag risky clauses, then summarization for the reviewer. Each step has its own error rate, so pipelines need to be evaluated end to end as well as step by step. Many teams now use an LLM to prelabel training data, then train a small model on those labels.
- Text classification: sentiment, topic, intent or spam detection.
- Named entity recognition: pulling names, dates, amounts and product codes from text.
- Machine translation between languages.
- Summarization of long documents, calls or email threads.
- Question answering and semantic search over a knowledge base.
- Speech recognition and text-to-speech for voice interfaces.
- Information extraction that turns free text into structured database records.
NLP vs large language models
Large language models are a product of NLP research, not a replacement for the field. An LLM can perform many NLP tasks from a single prompt, which makes it ideal for prototypes and varied inputs. For a narrow task at high volume, such as tagging a million support tickets a day, a small fine-tuned classifier such as a BERT variant is often cheaper, faster and easier to evaluate than calling a general LLM for every item.
Example: routing support tickets
A software company receives thousands of emails a week. An NLP model classifies each one by product area and urgency, extracts the account ID and order number, and detects frustrated sentiment. Tickets land in the right queue already tagged, and the most urgent appear first. Agents correct wrong labels inside the help desk tool, and those corrections become fresh training data for the next model version.
Language data is messy. Spelling errors, mixed languages such as Hinglish, sarcasm and domain jargon all reduce accuracy, and a model tested only on clean benchmark text will disappoint. Nexzem's NLP projects include evaluation sets built from the client's own messages, so performance is measured on the language customers actually use rather than on textbook examples.