Word embeddings are the base of most Machine Learning models that deal with text (bots, classification, sentiment, etc). But traditional embeddings have fundamental flaws in how they deal with basic content, assigning similar representations to opposite concepts (such as love vs. hate).
Our embeddings incorporate additional linguistic information to overcome these obstacles and provide a significant accuracy boost.
- Higher accuracy
- Broad coverage (formal & informal)
Download the Whitepaper to find out the main problems and solutions