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Related Experiment Video

Updated: Dec 11, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets.

Hoora Rezaei Moghaddam1, Sheela Ramanna2

  • 1Sightline Innovation Inc., 136 Market Avenue, Unit 300, Winnipeg, MB, R3B 0P4, Canada.

Patterns (New York, N.Y.)
|August 25, 2020
PubMed
Summary

This study introduces Tolerance Rough Set-based Pattern Learner 2.0 (TPL 2.0) for automated knowledge extraction from web data. TPL 2.0 shows promising precision in learning from large, noisy datasets.

Keywords:
granular computingmachine learningnamed entity recognitionnatural language processingsemi-supervised learningtolerance rough sets

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Mining

Background:

  • Automated knowledge extraction from web corpora aids natural language processing (NLP) applications, particularly question-answering systems.
  • Current methods offer minimal human intervention and scalable, automated learning of relational facts.
  • Challenges remain in handling large, noisy datasets and addressing concept drift.

Purpose of the Study:

  • To evaluate the scalability and concept drift performance of a tolerance rough set-based learner.
  • To introduce an improved semi-supervised learner, Tolerance Rough Set-based Pattern Learner 2.0 (TPL 2.0).
  • To adapt rough set methodology for categorizing linguistic patterns and extracting information from web data.

Main Methods:

  • Designing and implementing TPL 2.0, a new version of the semi-supervised tolerance rough set-based pattern learner.
  • Adapting tolerance rough set methodology for linguistic pattern categorization.
  • Extracting categorical information from a large, noisy dataset of crawled web pages.

Main Results:

  • TPL 2.0 demonstrated promising performance.
  • The learner was evaluated against three benchmark algorithms: Tolerant Pattern Learner 1.0, Fuzzy-Rough Set Pattern Learner, and Coupled Bayesian Sets-based learner.
  • Precision@30 was a key metric for comparison.

Conclusions:

  • TPL 2.0 shows potential for scalable knowledge repository construction from web corpora.
  • The adapted rough set methodology effectively categorizes linguistic patterns.
  • Further research can explore TPL 2.0's application in various NLP tasks requiring automated fact extraction.