What is the relationship between NLP and machine learning?

NLP defines tasks involving human language, while machine learning provides methods that learn patterns for many of those tasks from labelled or self-supervised data.

Is NLP a type of machine learning?

NLP is a field focused on language processing. It uses machine learning extensively but can also use linguistic rules, search methods, knowledge bases and deterministic software.

Which machine-learning algorithms are used in NLP?

Common approaches include linear classifiers, probabilistic models, boosting, recurrent and convolutional networks, transformers, embedding models and task-specific combinations.

Do all NLP projects need a large language model?

No. Rules, TF-IDF with a linear model or smaller pretrained encoders can be more accurate, faster and easier to govern for bounded tasks.

How is machine learning used in sentiment analysis?

A model learns from text labelled with defined sentiment categories, then predicts those labels for new text. Domain-specific labels, sarcasm and context require careful evaluation.

How should an NLP model be evaluated?

Use task-specific measures, per-class and per-language slices, calibrated operating thresholds, human review and the cost of downstream errors rather than one aggregate score.

How does an NLP system improve after launch?

It improves through monitored production evidence, adjudicated feedback, versioned data, regression testing and controlled releases—not merely by remaining online.

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Роль машинного обучения в обработке естественного языка

Обработка естественного языка (NLP) значительно изменилась с появлением машинного обучения. В этой статье рассматривается, как методы машинного обучения улучшают приложения НЛП, делая их более эффективными.