tutorial

Bringing structure to text: mining phrases, entities, topics, and hierarchies

Authors:

Jiawei Han,

Chi Wang,

Ahmed El-KishkyAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Page 1968

https://doi.org/10.1145/2623330.2630804

Published: 24 August 2014 Publication History

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Abstract

Mining phrases, entity concepts, topics, and hierarchies from massive text corpus is an essential problem in the age of big data. Text data in electronic forms are ubiquitous, ranging from scientific articles to social networks, enterprise logs, news articles, social media and general web pages. It is highly desirable but challenging to bring structure to unstructured text data, uncover underlying hierarchies, relationships, patterns and trends, and gain knowledge from such data.

In this tutorial, we provide a comprehensive survey on the state-of-the art of data-driven methods that automatically mine phrases, extract and infer latent structures from text corpus, and construct multi-granularity topical groupings and hierarchies of the underlying themes. We study their principles, methodologies, algorithms and applications using several real datasets including research papers and news articles and demonstrate how these methods work and how the uncovered latent entity structures may help text understanding, knowledge discovery and management.

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Supraja SKhong A(2024)Quad-Faceted Feature-Based Graph Network for Domain-Agnostic Text Classification to Enhance Learning EffectivenessIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.342163211:6(7500-7515)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3421632
Zhang JLi YShen FXia CTan HHe Y(2024)Hierarchy-Aware and Label Balanced Model for Hierarchical Text ClassificationKnowledge-Based Systems10.1016/j.knosys.2024.112153300:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112153
El-Kishky AKoehn PSchwenk HHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Searching the Web for Cross-lingual Parallel DataProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401417(2417-2420)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401417
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Index Terms

Bringing structure to text: mining phrases, entities, topics, and hierarchies
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2014

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

Acceptance Rates

KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Supraja SKhong A(2024)Quad-Faceted Feature-Based Graph Network for Domain-Agnostic Text Classification to Enhance Learning EffectivenessIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.342163211:6(7500-7515)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3421632
Zhang JLi YShen FXia CTan HHe Y(2024)Hierarchy-Aware and Label Balanced Model for Hierarchical Text ClassificationKnowledge-Based Systems10.1016/j.knosys.2024.112153300:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112153
El-Kishky AKoehn PSchwenk HHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Searching the Web for Cross-lingual Parallel DataProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401417(2417-2420)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401417
Senthil Kumar NDinakaran M(2020)An algorithmic approach to rank the disambiguous entities in Twitter streams for effective semantic search operationsSādhanā10.1007/s12046-019-1247-145:1Online publication date: 24-Jan-2020
https://doi.org/10.1007/s12046-019-1247-1
Huang WChen ELiu QChen YHuang ZLiu YZhao ZZhang DWang SZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Hierarchical Multi-label Text ClassificationProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357885(1051-1060)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357885
Kim HEl-Kishky ARen XHan J(2019)Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006049(105-114)Online publication date: Dec-2019
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Collins H(2018)The Use of Data and Readability Analytics to Assist Instructor and Administrator Decisions in Support of Higher Education Student Writing Skillsundefined10.12794/metadc1157590Online publication date: May-2018
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Sunena Kaur K(2016)Web cultural mining and enhancing user access on web using culture2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)10.1109/RTEICT.2016.7807880(542-545)Online publication date: May-2016
https://doi.org/10.1109/RTEICT.2016.7807880

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A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge

CitationLDA++: an Extension of LDA for Discovering Topics in Document Network

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