{'fraction_non_alphanumeric': 0.01658374792703151, 'fraction_numerical': 0.01658374792703151, 'mean_word_length': 5.425531914893617, '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}}
Automatic scientific text classification using local patterns: KDD CUP 2002 (task 1)

In this paper, we describe our approach for addressing Task 1 in the KDD CUP 2002 competition. The approach is based on developing and using an improved automatic feature selection method in conjunction with traditional classifiers. The feature selection method used is based on capturing frequently occurring keyword combinations (or motifs) within short segments of the text of a document and has proved to produce more accurate classification results than approaches relying solely on using keyword-based features.