Natural language processing (NLP), a branch of artificial intelligence, addresses the understanding of human language (both in written and spoken form) by computers. As a scientific discipline, NLP covers tasks such as determining sentence structures and boundaries in documents, detecting keywords or phrases in audio recordings, inferring relationships between documents, and uncovering meaning in informal or slang speech patterns. NLP can make it possible to analyze oral data and recognize patterns that are not currently structured.
NLP holds the key to enabling major advances in text analysis and gaining deeper and potentially more powerful insights from social media data streams where slang or non-traditional language is prevalent.
Mixed workload is the capacity to support multiple applications with different SLAs in a single environment.
Data Observability is the ability to monitor, diagnose, and manage the quality of data throughout the data lifecycle. It is also the discipline to automatically find out the health of your data and solve problems as soon as possible.
Latent Dirichlet Allocation (LDA) is a topic modeling technique that allows the discovery of hidden topic structures on large amounts of text data.
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