Title: Deep Learning for Language Understanding
Many current language understanding algorithms rely on expert knowledge to engineer models and features. In this talk, I will discuss how to use Deep Learning to understand texts without much prior knowledge. In particular, our algorithms will learn the vector representations of words. These vector representations can be used to solve word analogy or translate unknown words between languages. Our algorithms also learn vector representations of sentences and documents. These vector representations preserve the semantics of sentences and documents and therefore can be used for machine translation, text classification, information retrieval and sentiment analysis.