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Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study.

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    Lexical chain features, linking semantically related words, significantly improve text simplification by identifying difficult sentences. These features outperform standard methods, boosting accuracy in text difficulty classification.

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    Area of Science:

    • Computational Linguistics
    • Natural Language Processing
    • Text Simplification

    Background:

    • Text simplification aims to make complex texts more accessible.
    • Identifying text difficulty is crucial for effective simplification.
    • Existing methods often rely on basic lexical features.

    Purpose of the Study:

    • To discover data-driven features for text simplification.
    • To investigate the effectiveness of lexical chain features (exact, synonymous, semantic) for text difficulty assessment.
    • To compare lexical chain features against standard bag-of-words features.

    Main Methods:

    • A document-level corpus statistics study (914 texts) analyzed five features per lexical chain type.
    • A sentence-level classification task (11,000 sentences) evaluated feature usefulness.
    • Lexical chain features were compared with bag-of-words using various classifiers (logistic regression, SVM, random forest, etc.).

    Main Results:

    • Significant differences in average chain length and average number of cross chains were found between easy and difficult texts.
    • Lexical chain features significantly outperformed bag-of-words features across all tested classifiers.
    • The best classifier using lexical chain features achieved ~90% accuracy, compared to 78% for bag-of-words.

    Conclusions:

    • Lexical chain features provide valuable information for identifying text difficulty.
    • These features offer complementary information beyond standard lexical features for text simplification.
    • The study demonstrates the potential of lexical chains in enhancing automated text simplification systems.