Mining Structures of Factual Knowledge from Text : An Effort-Light Approach / by Xiang Ren, Jiawei Han.

Format:

Book

Author/Creator:

Ren, Xiang., Author.

Han, Jiawei., Author.

Series:

Synthesis Lectures on Data Mining and Knowledge Discovery, 2151-0075

Language:

English

Subjects (All):

Data mining.

Statistics.

Data Mining and Knowledge Discovery.

Local Subjects:

Data Mining and Knowledge Discovery.

Statistics.

Physical Description:

1 online resource (XV, 183 p.)

Edition:

1st ed. 2018.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

Mode of access: World Wide Web.

Summary:

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.

Contents:

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

Applications

Conclusions

Vision and Future Work

Bibliography

Authors' Biographies.

Notes:

Includes bibliographical references and index.

ISBN:

9783031019128

3031019121