Bayesian Optimization of Text Representations

Understand

When applying machine learning to problems in NLP, there are many choices to make about how to represent input texts.

• These choices can have a big effect on performance, but they are often uninteresting to researchers or practitioners who simply need a module that performs well.
• We propose an approach to optimizing over this space of choices, formulating the problem as global optimization.
• We apply a sequential model-based optimization technique and show that our method makes standard linear models competitive with more sophisticated, expensive state-of-the-art methods based on latent variable models or neural networks on various topic classification and sentiment analysis problems.