{'fraction_non_alphanumeric': 0.019744483159117306, 'fraction_numerical': 0.0, 'mean_word_length': 5.53030303030303, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Semantic role-based representations in text classification

Although good results for automatic text classification can be achieved with the use of bag-of-words representation, this model is not suitable for all classification problems and richer text representations can be required. In this paper, we proposed two text representation models based on semantic role labels and analyzed them in text classification scenarios. We also evaluated the combination of bag-of-words with a semantic representation considering ensemble multi-view strategies. We explored different classification problems for two text collections and pointed out situations that require more than a bag-of-words. The experimental evaluation indicates that the combination of bag-of-words and a text representation based on semantic role labels can improve text classification accuracies.