Phrase Mining from Massive Text and Its Applications

A lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people worldwide.A core challenge presents itself: How can one efficiently and effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining applications?

In this book, we investigated one promising paradigm for representing unstructured text, that is, through automatically identifying high-quality phrases from innumerable documents. In contrast to a list of frequent n-grams without proper filtering, users are often more interested in results based on variable-length phrases with certain semantics such as scientific concepts, organizations, slogans,and so on. We propose new principles and powerful methodologies to achieve this goal, from the scenario where a user can provide meaningful guidance to a fully automated setting through distant learning. This book also introduces applications enabled by the mined phrases and points out some promising research directions.

Jialu Liu, an engineer at Google Research in New York, is working on structured data for knowledge exploration. He received his B.Sc. from Zhejiang University, China, in 2007 and Ph.D. degree in computer science from the University of Illinois at Urbana-Champaign in 2015. His research has been focused on scalable data mining, text mining, and information extraction.Jingbo Shang, is a Ph.D. candidate in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He received a B.Sc. from Shanghai Jiao Tong University, China in 2014. His research focuses on mining and constructing structured knowledge from massive text corpora.Jiawei Han, Abel Bliss Professor, Department of Computer Science, the University of Illinois, has been researching data mining, information network analysis, and database systems, and has been involved in over 700 publications. He served as the founding Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (TKDD). Jiawei received the ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), and IEEE Computer Society W. Wallace McDowell Award (2009). He is a Fellow of ACM and a Fellow of IEEE. His co-authored textbook, Data Mining: Concepts and Techniques (Morgan Kaufmann), has been adopted worldwide.