As humans, we can recognize and compare words, but machines have difficulty decoding their meaning. That’s why NLP tools need word embeddings, which represent the meaning of words as numeric vectors that can be added, subtracted, and compared mathematically. 

In this webinar, Babel Street Chief Scientist Kfir Bar explains the concepts around how word embeddings and semantic similarity and apply to real-life and mission-critical situations.