Using InterSystems Natural Language Processing (NLP)

You can extract entities from unstructured text data using semantic analysis, which identifies and indexes concepts, relations, and paths within the text. This translates the content into a relational network for analysis without requiring prior knowledge of the material.

Yes, natural language processing supports analyzing SQL records and web feeds for semantic relationships. It processes various text source types including files, SQL records, and web feeds to identify semantic entities and concepts within the content.

Smart matching is the best way to tag text entities against user-defined lists. It matches text entities from your unstructured data against these lists to achieve high-precision tagging and accurate entity association.

Yes, InterSystems IRIS NLP supports text analysis in multiple languages. It analyzes unstructured text data to identify semantic entities, concepts, relations, and paths across various supported languages without requiring prior language knowledge.

You can identify recurring themes in customer feedback by applying smart indexing to translate unstructured text into a relational network. This facilitates content analysis and entity association to surface themes without manually reading each comment.