{'fraction_non_alphanumeric': 0.024, 'fraction_numerical': 0.0, 'mean_word_length': 5.68, '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}}
SVM-based clause-dependency determination in syntactic analysis

In syntactic analysis of a long sentence, it is difficult to determine the dependency among clauses. To handle such syntactic ambiguity among clauses, this paper proposes a machine learning-based determination method of clause-dependency. We extract various features from clauses, and experiment using SVM machine learning algorithm. Our experimental results showed that the proposed SVM-based method outperformed the previous methods.