Mining Structures of Factual Knowledge from Text

An Effort-Light Approach

AvXiang Ren,Jiawei Han

Häftad, Engelska, 2018

Del i serien Synthesis Lectures on Data Mining and Knowledge Discovery

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Beskrivning

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-valuemining and information extraction. This book introduces this new research frontier and points out some promising research directions.

Produktinformation

Utgivningsdatum:2018-06-26
Mått:191 x 235 x 12 mm
Vikt:384 g
Format:Häftad
Språk:Engelska
Serie:Synthesis Lectures on Data Mining and Knowledge Discovery
Antal sidor:183
Förlag:Springer International Publishing AG
ISBN:9783031007842
Originaltitel:Mining Structures of Factual Knowledge from Text

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik
Databaser inom Data och IT

Mer om författaren

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 Computer Science from University of Illinois at Urbana-Champaign. His research develops computational methods and systems that extract machine-actionable 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.Jiawei Han is the Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab (2009-2016), and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing since 2014. He is a Fellow of ACM, a Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, and 2009 M. Wallace McDowell Award from IEEE Computer Society. His co-authored book Data Mining:Concepts and Techniques has been adopted as a popular textbook worldwide.

Innehållsförteckning

Acknowledgments.- Introduction.- Background.- Literature Review.- Entity Recognition and Typing with Knowledge Bases.- Fine-Grained Entity Typing with Knowledge Bases.- Synonym Discovery from Large Corpus.- Joint Extraction of Typed Entities and Relationships.- Pattern-Enhanced Embedding Learning for Relation Extraction.- Heterogeneous Supervision for Relation Extraction.- Indirect Supervision: Leveraging Knowledge from Auxiliary Tasks.- Mining Entity Attribute Values with Meta Patterns.- Open Information Extraction with Global Structure Cohesiveness.- Open Information Extraction with Global Structure Cohesiveness.- Applications.- Conclusions.- Vision and Future Work.- Bibliography.- Authors' Biographies.