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Tag: Vector

Word Embeddings for Fuzzy Matching of Organization Names

Rosette’s name matching is enhanced by word embeddings to match based on semantics as well as phonetics Tracking mentions of particular organizations across news articles, social media, and internal communications is integral to the workflow of dozens of use-cases across industries. However it can be especially challenging to match names of companies and organizations because […]

Using Deep Learning to Power Multilingual Text Embeddings for Global Analysis, Part II

Wait! Have you read Part I yet? Check it out, then come on back.  Putting Text Embeddings to Work Using the updated text embeddings endpoint in Rosette API 1.5, you’ll notice significant accuracy improvements on longer strings of text, both sentences and documents. We’ve also begun to incorporate text embeddings into some of our higher […]

Using Deep Learning to Power Multilingual Text Embeddings for Global Analysis, Part I

A Crash Course in Basic Text Embeddings A chronic problem with using machines to analyze human language is that the same meaning can be expressed using many different words. Take for example the sentence “Bill Gates was educated at Harvard.”  There are many ways to express this relationship: Bill Gates studied at Harvard, Bill Gates […]