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Learning from Large, Complex Data with Applications in Natural Language Processing

Machine learning techniques have been widely applied in many domains. For example, they have been shown to be effective in resolving ambiguity and modeling linguistic structure in human languages, leading to substantial advancements in information extraction, question answering, and machine translation. However, in many cases, high accuracy requires training on large amounts of data, exploiting various sources of learning signals and/or exploring complex input and output interactions. My research goal is to design practical algorithms to efficiently learn expressive models from large-scale data and massive quantities of knowledge. My study benefits both the theory and the practice of Machine Learning (ML) and helps application domains such as Natural Language Processing (NLP), where researchers often encounter large, complex data.