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Related Experiment Video

Updated: Mar 28, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.3K

Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach.

Xiang Ren1, Ahmed El-Kishky1, Chi Wang2

  • 1University of Illinois at Urbana-Champaign, Urbana, IL, USA.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|December 26, 2015
PubMed
Summary

This tutorial introduces data-driven methods for recognizing typed entities in large text datasets. These techniques automatically identify and label entities, aiding knowledge discovery and management in diverse information sources.

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Area of Science:

  • Computer Science
  • Information Science
  • Natural Language Processing

Background:

  • Modern society generates vast amounts of unstructured text data from diverse sources like news, social media, and scientific literature.
  • Extracting meaningful information and understanding relationships between entities within this data is crucial for knowledge discovery and management.

Purpose of the Study:

  • To introduce data-driven methods for recognizing typed entities in massive, domain-specific text corpora.
  • To demonstrate the scalability and effectiveness of these methods for identifying and labeling entities.

Main Methods:

  • Utilizing data-driven approaches to automatically identify token spans as entity mentions within documents.
  • Implementing methods for labeling identified entities with specific types (e.g., people, products, food).

Related Experiment Videos

Last Updated: Mar 28, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.3K
  • Applying these techniques to large-scale, domain-specific text collections.

    • Main Results:

    • Successful identification and typing of entities in real-world datasets, including news articles and tweets.
    • Demonstration of scalable entity recognition across different text domains.
    • Validation of the utility of typed entities for knowledge discovery and management.

    Conclusions:

    • Data-driven entity recognition is a powerful technique for unlocking value from unstructured text data.
    • The presented methods offer a scalable solution for identifying and categorizing entities in massive corpora.
    • Typed entities significantly enhance knowledge discovery and management processes in information-rich environments.