Highlights

• Leverage FactSet’s Machine Learning-based Named Entity Recognition service to identify people and company names, mapped to FactSet Entity Identifiers in any text content, such as News, Research Reports, Transcripts, or Filings.
• Conduct your analysis within your Snowflake environment, reducing burdensome ETL processes and saving time.
• Rely on a single system for your data collection and tagging needs. Optimize the organization of your internal data assets and 3rd party data to increase efficiency, transparency, and scalability. Text content such as news articles and websites contains a large amount of useful company and people information. To extract that information with minimal effort, leverage FactSet’s Named Entity Recognition (NER) service. Using Natural Language Processing and other AI techniques, NER easily finds company and people names in plain text or HTML content and matches those names with FactSet identifiers. Offered as an external function directly in your Snowflake, FactSet’s Named Entity Recognition (NER) service can seamlessly process text, streamlining entity detection and entity mapping. This allows you to link documents you write, edit, or collect with any other FactSet datasets that are linked to FactSet IDs, such as fundamentals or ownership