Knowledge Base Population

This course will introduce state-of-the-art Information Extraction (IE) and Knowledge Base Population (KBP) techniques. In the first lecture, we will focus on the quality issue of IE and KBP. We will give a comprehensive overview about the successful methods for each task. We will review where we have been (the most successful methods in literature), and where we are going (the remaining challenges, and novel methods to tackle these challenges). In the second lecture, we will focus on the portability issue, namely how to rapidly build a new IE/KBP system for a new language, domain, genre within a short time with low cost. We will introduce a brand new “Liberal” Information Extraction (IE) paradigm to combine the merits of traditional IE (high quality and fine granularity) and Open IE (high scalability). Liberal IE aims to discover schemas and extract facts from any input corpus, without any annotated training data or predefined schema.