{'fraction_non_alphanumeric': 0.014814814814814815, 'fraction_numerical': 0.0, 'mean_word_length': 6.118421052631579, '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}}
Machine Learning Experiments for Textual Entailment

We present a system that uses machine learning algorithms to combine features that capture various shallow heuristics for the task of recognizing textual entailment. The features quantify several types of matches and mismatches between the test and hypothesis sentences. Matching features represent lexical matching (including synonyms and related words), part-ofspeech matching and matching of grammatical dependency relations. Mismatch features include negation and numeric mismatches.