{'fraction_non_alphanumeric': 0.014301430143014302, 'fraction_numerical': 0.0044004400440044, 'mean_word_length': 5.232876712328767, '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}}
Style Breach Detection: An Unsupervised Detection Model

This paper deals with the sub-task of PAN 2017 Author Identification, which is to detect style breaches for unknown number of authors within a single document in English. The presented model is an unsupervised approach that will detect style breaches and mark text boundaries on the basis of different stylistic features. This model will use some classical stylistic features like POS analysis and sentence lexical analysis. Also some new features naming common English word frequencies within sentence text, sentence expression and sentence attitude have been proposed. The new features may not be directly linked to author’s style of writing but to the subject/topic of sentence under analysis. Moreover the model uses sentence window for style detection. The sentence window may be extended to neighboring sentences during its unsupervised analysis.