Natural Language Processing (NLP)
Techniques and tools that enable machines to interpret, generate, and analyze human language in text or speech form.
A field combining linguistics and machine learning to process unstructured language data - tasks include tokenization, part-of-speech tagging, parsing, named-entity recognition, sentiment analysis, and language generation. Governance focuses on data provenance, bias in language models, privacy of sensitive text, and rigorous evaluation on diverse linguistic datasets to ensure robust, fair, and secure language capabilities.
A customer-service center uses an NLP pipeline to auto-route inquiries: text is tokenized and classified into “billing,” “technical support,” or “feedback.” The system flags low-confidence classifications for human review. Quarterly audits verify that routing accuracy remains above 90% across all languages supported, preventing misdirected requests.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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