You need solid syntax parsing to really understand the nuance of language. Complicated negation patterns, relationships between entities, entity sentiment assignment (and many other things) are all examples for which having sophisticated syntax understanding is important. The question then is how to get an understanding of syntax across many languages, content types, and contexts. Most traditional model-based approaches require manually coded syntax trees, which are costly to generate, as they require relatively expensive linguist time. These trees exist for some languages, and some content types; but not for, say, German Tweets, or Swedish biotech. It turns out that the problem can be stated as a “similarity” problem, which then looks like a recommendation problem. This presentation will discuss how we leveraged a matrix factorization recommendation algorithm to create a highly efficient, easily extensible syntax parser.