In today's computerized and information-based society, people are inundated with vast amounts of text data, ranging from news articles, social media posts, scientific publications, to a wide range of textual information from various vertical domains (e.g., corporate reports, advertisements, legal acts, medical reports). How to turn such massive and unstructured text data into structured, actionable knowledge, and how to enable effective and user-friendly access to such knowledge is a grand challenge to the research community. This course will introduce and discuss many of the sub-problems and machine learning approaches for knowledge extraction and reasoning, including use of language features, sequence learning models, rule learning, relational learning, and deep learning techniques. We will discuss segmentation of text sequences, classification of segments into types, clustering and de-duplication of records, knowledge graph embedding, knowledge reasoning.


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