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PRo-Pat: Probabilistic Root-Pattern Bi-gram data language model for Arabic based morphological analysis and

Bassam Haddad1, Ahmad Awwad2, Mamoun Hattab3

  • 1Department of Data Science and Artificial Intelligence, University of Petra, Amman, Jordan.

Data in Brief
|January 23, 2023
PubMed
Summary
This summary is machine-generated.

A new Arabic bi-gram language model, focusing on the root-pattern paradigm, was developed using extensive data. This model aids in reducing ambiguity in Arabic morphological analysis and enhances understanding of word perception.

Keywords:
Arabic language modelN-gram modelsProbabilistic morphologyRoot Pattern AnalysisRoot-Pattern ClassificationWord Cognition

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Natural Language Processing

Background:

  • Arabic morphology is complex, with the root-pattern system being central to word formation.
  • Previous research has underutilized Maximum Likelihood Estimation (MLE) at the root-pattern level for Arabic.
  • Ambiguity in Arabic morphological analysis impacts cognitive processing and NLP applications.

Purpose of the Study:

  • To construct a comprehensive bi-gram language model for Arabic, emphasizing the root-pattern paradigm.
  • To estimate root-pattern distributions, including conditional probabilities like P(root|pattern) and P(pattern|pattern).
  • To provide a valuable resource for researchers investigating Arabic cognitive aspects and NLP challenges.

Main Methods:

  • Processing 29,192,662 HTML files from ClueWeb to create a large Arabic corpus.
  • Morphological analysis of approximately 18.5 million words to extract root-pattern occurrences.
  • Construction of a bi-gram language model with over 9.3 million entries, including conditional probabilities.

Main Results:

  • A dataset of 9,311,246 morphologically analyzed wordform bi-grams was generated.
  • Conditional probabilities for root-pattern relationships (e.g., P(root|pattern)) were estimated.
  • The model covers 8086 roots and 20413 pattern-forms, providing detailed insights into Arabic morphology.

Conclusions:

  • The developed Arabic bi-gram language model, centered on the root-pattern phenomenon, offers a novel approach to morphological analysis.
  • This model effectively reduces ambiguities in Arabic, benefiting cognitive research and NLP tasks.
  • The data generated are beneficial for studies on visual word cognition, morpho-phonetic perception, and spell-checking in Arabic.