vocab_38201 = {'although': 1, 'back': 1, 'bend': 1, 'bicycl': 1, 'correct':
    1, 'dig': 1, 'general': 1, 'handlebar': 1, 'hefti': 1, 'lever': 1, 'nt':
    2, 'quit': 1, 'rear': 1, 'spade': 1, 'sprung': 1, 'step': 1, 'type': 1,
    'use': 1, 'wo': 1}
vocab_38202 = {'caus': 1, 'ear': 1, 'energi': 1, 'experi': 1, 'inner': 1,
    'light': 1, 'nervous': 1, 'part': 1, 'qualiti': 1, 'reach': 1,
    'receptor': 2, 'retin': 1, 'sensori': 1, 'stimul': 2, 'system': 2,
    'upon': 2, 'wo': 1}
Implement K Neighbors Classifier and Linear SVM in scikit-learn for Word sense disambiguiation