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Modeling Topics in DFA-Based Lemmatized Gujarati Text.

Uttam Chauhan1, Shrusti Shah1, Dharati Shiroya1

  • 1Department of Computer Engineering, Vishwakarma Government Engineering College, Chandkheda, Ahmedabad 382424, India.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deterministic finite automaton (DFA) based lemmatization technique for Gujarati text. This method enhances topic modeling by improving the interpretability and semantic coherence of discovered topics.

Keywords:
Gujarati text lemmatizationLatent Dirichlet Allocationoverly general topicspoor quality topicstopic models

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Topic modeling algorithms map high-dimensional text corpora to lower-dimensional topical subspaces.
  • Topic interpretability is often hindered by large vocabularies and inflectional word forms, especially in morphologically rich languages.
  • Existing topic models rely on term co-occurrence, which can be weakened by linguistic variations.

Purpose of the Study:

  • To propose a deterministic finite automaton (DFA) based lemmatization technique for the Gujarati language.
  • To improve the quality and interpretability of topics discovered through topic modeling on Gujarati text.
  • To evaluate the impact of lemmatization on vocabulary size and topic semantic coherence.

Main Methods:

  • Developed a deterministic finite automaton (DFA) for lemmatizing Gujarati words to their root forms.
  • Applied the lemmatization technique to a Gujarati text corpus.
  • Inferred topics using topic modeling on both lemmatized and unlemmatized corpora.
  • Utilized statistical divergence measurements (Log Conditional Probability, Pointwise Mutual Information, Normalized Pointwise Mutual Information) to assess topic coherence.

Main Results:

  • Lemmatization reduced the Gujarati vocabulary size by 16%.
  • Semantic coherence of topics significantly improved after lemmatization across all three evaluated metrics.
  • Log Conditional Probability improved from -9.39 to -7.49.
  • Pointwise Mutual Information improved from -6.79 to -5.18.
  • Normalized Pointwise Mutual Information improved from -0.23 to -0.17.

Conclusions:

  • The proposed DFA-based lemmatization technique effectively addresses challenges in topic modeling for morphologically rich languages like Gujarati.
  • Lemmatization leads to more interpretable and semantically coherent topics compared to using raw, unlemmatized text.
  • This approach offers a valuable method for enhancing unsupervised machine learning tasks on inflectionally diverse languages.