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Comparing methods for single paragraph similarity analysis.

Benjamin Stone1, Simon Dennis, Peter J Kwantes

  • 1School of Psychology, The University of AdelaideDepartment of Psychology, Ohio State UniversityDefence Research and Development Canada (Toronto).

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|August 29, 2014
PubMed
Summary
This summary is machine-generated.

Simple semantic models outperform complex ones for paragraph similarity. Optimizing corpus creation, like using Wikipedia data, further enhances these estimates for better text analysis.

Keywords:
Corpus constructionCorpus preprocessingParagraph similaritySemantic modelsWikipedia corpora

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Information Retrieval

Background:

  • Evaluating semantic similarity is crucial for understanding text.
  • Previous research focused on smaller text units, yielding different results.
  • The effectiveness of various semantic models for paragraph-level similarity is underexplored.

Purpose of the Study:

  • To compare the performance of six semantic models against human judgments of paragraph similarity.
  • To investigate the impact of corpus creation strategies on semantic model performance.
  • To identify optimal methods for estimating semantic similarity at the paragraph level.

Main Methods:

  • Six semantic models (word overlap, vector space, LSA, Topic Model, SpNMF, CSM) were applied to two news datasets.
  • Model-generated similarity scores were compared with human ratings.
  • Corpus creation techniques, including data cleaning, document truncation, and automated Wikipedia-based corpus generation, were systematically evaluated.

Main Results:

  • Simple models (word overlap, vector space) provided superior similarity estimates for single paragraphs compared to complex models.
  • Text preprocessing steps like removing numeric/single characters and truncating document length improved model performance.
  • Automated Wikipedia-based corpora, augmented with dataset paragraphs, significantly enhanced model effectiveness.

Conclusions:

  • For paragraph-level semantic similarity, simpler models are more effective than complex ones.
  • Strategic corpus creation and augmentation are vital for improving semantic model accuracy.
  • Findings offer practical guidance for enhancing text analysis and information retrieval systems.