Text Analytics

Train Powerful Language Models, Faster, and More Reliably with the World's Largest Annotated Topic Database

Request information

Built for Scale

Used for our own language models, RavenPack Text Analytics are specifically designed to address the challenges of working with unstructured textual content in the business, finance, social, and legal sectors — from historical archives to real-time feeds. Pre-annotated by hundreds of thousands of rules, they give you the highest-quality training data.

We spent over a decade crafting the best-in-class text analytics to build NLP models, so you don't have to.

Why it works

Streamline your machine learning workflows for shorter time to market with the only text analytics infrastructure that delivers:

How our Text Analytics are Prepared

RavenPack Text Analytics are produced by our proven natural language processing infrastructure that has processed terabytes of unstructured data over 15 years.

Content Ingestion & Schema Normalization From existing web and filings feeds, or from your own content in over 1,000 formats, RavenPack turns texts into a unified schema ready for NLP tasks with a single representation, bounding boxes, story, paragraph, and sentence coordinates. Entity Extraction, Co-referencing & Knowledge Graph Detection RavenPack identifies, co-references, and tags entities and concepts from our constantly improving RavenPack Knowledge Graph of 12 Million entities including Companies, Places, People. Extra-Large Scale Topic Classification & Relationship Extraction Using millions of pre-curated semantic templates, RavenPack identifies 7,400 topics and how entities relate to those topics. The taxonomy covers business, legal, society, ESG, and more. Sentiment, Relevance & Other Analytics Proprietary algorithms then process each sentence, entity, and topic detected for Relevance, Sentiment, Novelty, and Similarity metrics for powerful deduplication analytics. Knowledge Graph & Data Feed Output Knowledge graph and archives are historically generated, version controlled, and deployed via APIs with daily and even real-time updates available Content Ingestion & Schema Normalization From existing web and filings feeds, or from your own content in over 1,000 formats, RavenPack turns text into a unified schema ready for NLP tasks with a single representation, bounding boxes, story, paragraph, and sentence coordinates. Entity Extraction, Co-referencing & Knowledge Graph Detection RavenPack identifies, co-references, and tags entities and concepts from our constantly improving RavenPack Knowledge Graph of 12 Million entities including Companies, Places, People. Extra-Large Scale Topic Classification & Relationship Extraction Using millions of pre-curated semantic templates, RavenPack identifies 7,400 topics and how entities relate to those topics. The taxonomy covers business, legal, society, ESG, and more. Sentiment, Relevance & Other Analytics Proprietary algorithms then process each sentence, entity, and topic detected for Relevance, Sentiment, Novelty, and Similarity metrics for powerful deduplication analytics. Knowledge Graph & Data Feed Output Knowledge graph and archives are historically generated, version controlled, and deployed via APIs with daily and even real-time updates available
Read More

Stay in the loop

Explore the significance of training data and its pivotal role in advancing Large Language Models with these articles by RavenPack.

Explore all published research

Request more Information