Advancements in Natural Language Processing (NLP) and Text Analytics
summary
Traces NLP’s evolution from rule-based grammars to transformer-era models, contrasts classic statistical pipelines with large language models, and lays out concrete preprocessing, annotation and deployment steps; includes applied case studies in healthcare, finance and customer service showing production pipelines and automation wins rather than theory alone. Highlights sentiment analysis and named entity recognition, names state-of-the-art model families and toolchains, notes common data quality bottlenecks and integration patterns and reports that deployed systems do reduce manual review and speed up workflows.
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