NLP with characters
We present a neural network model that computes embeddings of words using recurrent network based on long short-term memories to read in characters. As an alternative to word lookup tables that require a set of parameters for every word type in the vocabulary, our models only require a look up table for characters and a fixed number of parameters for the compositional model, independent of the vocabulary size. As a consequence, our model uses fewer parameters and is also sensitive to lexical content, such as morphology, making it more adequate for tasks where morphological information is required. In part-of-speech tagging, we can perform competitively with state-of-the-art systems, without explicitly engineering lexical features, and using a relatively small number of parameters.
Bio: Wang Ling is a student of the dual Ph.D. program in Computer Science between Carnegie Mellon University and Instituto Superior Técnico, where he also received his master degree in 2009. His Ph.D. work focuses on Machine Translation in noisy domains, such as Twitter, and Deep Learning for NLP.