Exploring Weights of Hierarchical and Equivalency Relationship in General Persian Texts

ABSTRACT

A thesaurus is a reference work that lists words grouped together according to similarity of meaning (containing synonyms and sometimes antonyms), in contrast to a dictionary, which contains definitions and pronunciations. Three kinds of relationships used in a thesaurus includes: (1) equivalency, (2) hierarchy, and finally (2) association. This paper proposes a novel method to develop a classification task in general Persian context while it employs a thesaurus. Two kinds of word relationships are employed in our used thesaurus: (1) equivalency, and (2) hierarchy. Each of these kinds has a weight that can be tuned. The paper explores all possible weights for the proper ones. After that a feature selection mechanism is also employed. A host of machine learning algorithms are employed as the classifier over the frequency based features. Experimental results indicate the usage of the best weights for these relationships; can lead to a good result.

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Index Terms

• Applied computing

  • Document management and text processing

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Language resources

  • Machine learning

    • Machine learning approaches

      • Neural networks

• Hardware

  • Emerging technologies

    • Circuit substrates

      • Cellular neural networks

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory