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speech from MathCat \n4. node compareFormulas.js : run the speech comparison metrics \n\n# Accessibility Speech Evaluation\n\nComparison of speech output for accessibility on Wikipedia paper with text similarity algorithms. \n\n\nMeasures: \n\nCharacter N-Gram: \n- Create N-Grams for formulas and calculate their overlap similarity based on sliding window\n- Consideration: Does not consider bigger changes in the word order and will not capture semantic similarity in all cases\n\nLevenshtein-Distance:\n- Levenshtein distance is simple and effective for measuring the minimum edit operations (insertions, deletions, substitutions) needed to transform one formula into another.\n- Consideration: It treats all words or characters equally, which might not capture the semantic or structural similarity\n\nJaccard Similarity: \n- Captures overlap in word sets\n- Consideration: It doesn't consider word order or frequency.\n\nCosine Similarity:\n- Cosine similarity considers the angle between formula vectors, making it useful for capturing semantic similarity and ignoring word order. \n- Consideration: It doesn't account for word repetitions or differences in formula length.\n\n\nTF/IDF Similarity: \n- TF-IDF is useful for capturing word importance relative to a document or set of documents.\n- Consideration: It might not handle short formula texts well, and it doesn't capture semantic or structural relationships\n\n# MathCatForPython usage \nParts of the code for speech generation 'generateSpeechForMathML.py' and the Rules\nas well as libmathcat.pyd are used from MathCATForPython by Neil Soiffer. \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhyper-node%2Faccessibilityspeecheval","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhyper-node%2Faccessibilityspeecheval","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhyper-node%2Faccessibilityspeecheval/lists"}