Bayesian Optimization of Text Representations

When applying machine learning to prob-lems in NLP, there are many choices to make about how to represent input texts. They can have a big effect on perfor-mance, but they are often uninteresting to researchers or practitioners who simply need a module that performs well. We ap-ply sequential model-based optimization over this space of choices and show that it makes standard linear models competitive with more sophisticated, expensive state-of-the-art methods based on latent variables or neural networks on various topic classifica-tion and sentiment analysis problems. Our approach is a first step towards black-box NLP systems that work with raw text and do not require manual tuning.