{'fraction_non_alphanumeric': 0.02707275803722504, 'fraction_numerical': 0.00676818950930626, 'mean_word_length': 4.92, '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}}
Chinese Unknown Word Extraction by Mining Maximized Substrings

The issue of identifying out-of-vocabulary (OOV) words is a major difficulty in Chinese word segmentation. We address this issue by applying a very efficient algorithm for extracting maximized substrings (Shen et al., 2013) from a large-scale raw text, which form a list of unknown word candidates. We then apply techniques such as Short-term Store and Lexicon-based Voting to reduce the noises in the extracted list of unknown words. We demonstrate that our method outperforms previous studies in both accuracy and efficiency.