Mining Structures of Factual Knowledge from Text: An Effort-Light Approach (Synthesis Lectures on Data Mining and Knowledge Discovery)

Ren, Xiang

ISBN 10: 1681733943 / ISBN 13: 9781681733944

Published by Morgan Claypool, 2018

About this Item

Seller Inventory # 63N94_48_1681733943

Bibliographic Details

• Title: Mining Structures of Factual Knowledge from Text: An Effort-Light Approach (Synthesis Lectures on Data Mining and Knowledge Discovery)
• Publisher: Morgan Claypool
• Publication Date: 2018
• Language: English
• ISBN 10: 1681733943
• ISBN 13: 9781681733944
• Binding: Hardcover
• Condition: new

About this title

Synopsis

The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora.

Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.

About the Author

Xiang Ren is an Assistant Professor in the Department of Computer Science at USC, affiliated faculty at USC ISI, and a part-time data science advisor at Snap Inc. At USC, Xiang is part of the Machine Learning Center, NLP community, and Center on Knowledge Graphs. Prior to that, he was a visiting researcher at Stanford University, and received his Ph.D. in CS@UIUC. His research develops computational methods and systems that extract machineactionable knowledge from massive unstructured data (e.g., text data), and particular focuses on problems in the space of modeling sequence and graph data under weak supervision (learning with partial/noisy labels, and semi-supervised learning) and indirect supervision (multi-task learning, transfer learning, and reinforcement learning). Xiang's research has been recognized with several prestigious awards including a Yahoo!-DAIS Research Excellence Award, a Yelp Dataset Challenge award, a C. W. Gear Outstanding Graduate Student Award and a David J. Kuck Outstanding M.S. Thesis Award. Technologies he developed have been transferred to U.S. Army Research Lab, National Institute of Health, Microsoft, Yelp, and TripAdvisor.