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Natural Language Processing (NLP) in plain language
Language depends on context. “The service is not bad” differs from “the service is bad”, even though most words are shared. An NLP task needs evaluation that reflects such distinctions.
Not every language problem needs a generative model. A constrained extraction rule may be enough for a standard identifier, while varied messages may need a learned method.
How it works
Define the language task
Specify the required labels, fields or generated response and the supported languages.
Represent and process the input
Choose appropriate text handling and a method that preserves information needed for the task.
Evaluate meaning, not just format
Test negation, ambiguity, spelling variation and mixed-language inputs relevant to your users.
Illustrative example · Fictional data
Recognising the actual support request
- Input
- A fictional message says: “The payment did not fail; I need a copy of the invoice.”
- Process
- Identify the request for an invoice instead of routing solely on the word “fail”.
- Expected output
- The message is routed to invoice assistance rather than failed-payment support.
- How to check it
- Compare with “The payment failed and I have no invoice.” Similar words express a different issue; a keyword match alone may miss it.
Where it helps
- Extracting fields from messages.
- Classifying requests by intent.
- Translating or summarising text for a defined audience.
NLP, LLMs and speech recognition
- NLP
- The broad field of language-processing tasks and methods.
- LLM
- A model family that can support many NLP tasks.
- Speech recognition
- Converts speech to text, which may feed a further language-processing task.
Named entities, sentiment and intent
Named-entity recognition marks spans such as organisations, locations and dates; entity linking resolves a mention to a known record. Sentiment classification estimates an expressed attitude, while intent classification identifies the requested action. A negative support message can still have the intent “change delivery address”—these labels answer different questions.
For a stable, narrow classification task, rules or a smaller supervised model may be cheaper and easier to evaluate than a general-purpose LLM. Generative models can handle flexible instructions and produce summaries, but need grounding and output checks. Compare both against representative examples.
Indian languages, code-switching and transliteration
A message may mix English and Hindi, use a regional language in Latin script, or contain domain abbreviations and spelling variations. Script detection alone does not identify the language or intent. Keep code-switched and transliterated examples in the evaluation set rather than translating every test into clean English.
Measure performance by language, script and task, with reviewers who understand the actual usage. Check names, negation, honorifics and domain vocabulary. A multilingual model’s general benchmark score does not establish quality for your customers’ messages or a specific regional-language workflow.
Related reading and learning pathways
Common pitfalls
- Negation and context can reverse a simple interpretation.
- Performance can differ across languages and dialects.
- A grammatically correct summary can omit an important exception.
Put the idea to work
Try it yourself
Write five versions of an invoice request, including a typo and a negated payment failure. Define the intended category before testing a method.
Common questions
Is NLP the same as generative AI?
No. NLP includes language analysis that does not generate content; generative AI also covers media beyond language. Classic NLP tasks include sentiment analysis, named-entity recognition, classification and translation. Modern large language models can perform many of these tasks as well as generate text.
Does one English test set establish multilingual quality?
No. Evaluate the languages and input styles the application must support. Performance often drops for other languages, mixed-language text such as Hinglish, informal spelling and domain jargon. Build test sets that reflect your real users. Include native-speaker review for languages where automated metrics are weak.
Your next step
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Connected concepts
Sources and further reading
Examples on this page are teaching exercises using fictional information. They are not client case studies or measured outcomes.