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Multidimensional Mining of Massive Text Data

  • Book
  • © 2019

Overview

Authors:
  1. Chao Zhang

    1. Georgia Institute of Technology, USA

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  2. Jiawei Han

    1. University of Illinois at Urbana-Champaign, USA

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About this book

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.

This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.

The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction

    • Chao Zhang, Jiawei Han
    Pages 1-10

  3. Cube Construction Algorithms

    1. Front Matter

      Pages 11-11
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    2. Topic-Level Taxonomy Generation

      • Chao Zhang, Jiawei Han
      Pages 13-30

    3. Term-Level Taxonomy Generation

      • Jiaming Shen
      Pages 31-48

    4. Weakly Supervised Text Classification

      • Yu Meng
      Pages 49-70

    5. Weakly Supervised Hierarchical Text Classification

      • Yu Meng
      Pages 71-87

  4. Cube Exploitation Algorithms

    1. Front Matter

      Pages 89-89
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    2. Multidimensional Summarization

      • Fangbo Tao
      Pages 91-116

    3. Cross-Dimension Prediction in Cube Space

      • Chao Zhang, Jiawei Han
      Pages 117-141

    4. Event Detection in Cube Space

      • Chao Zhang, Jiawei Han
      Pages 143-163

    5. Conclusions

      • Chao Zhang, Jiawei Han
      Pages 165-167

  5. Back Matter

    Pages 169-183
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Authors and Affiliations

  • Georgia Institute of Technology, USA

    Chao Zhang

  • University of Illinois at Urbana-Champaign, USA

    Jiawei Han

About the authors

Chao Zhang is an Assistant Professor in the School of Computational Science and Engineering, Georgia Institute of Technology. His research area is data mining and machine learning. He is particularly interested in developing label-efficient and robust learning techniques, with applications in text mining and spatiotemporal data mining. Chao has published more than 40 papers in top-tier conferences and journals, such as KDD, WWW, SIGIR, VLDB, and TKDE. He is the recipient of the ECML/PKDD Best Student Paper Runner-up Award (2015), Microsoft Star of Tomorrow Excellence Award (2014), and the Chiang Chen Overseas Graduate Fellowship (2013). His developed technologies have received wide media coverage and been transferred to industrial companies. Before joining Georgia Tech, he obtained his Ph.D. in Computer Science from University of Illinois at Urbana-Champaign in 2018.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.

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