{'fraction_non_alphanumeric': 0.02702702702702703, 'fraction_numerical': 0.0, 'mean_word_length': 7.181818181818182, '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}}
Textual Information Access: Statistical Models

Statistical models recently developed in several research communities (natural language processing, information retrieval, machine learning) for textual information access pertain to several application domains: Information extraction and retrieval, Text classification and clustering, Opinion mining, Comprehension aids (automatic summarization, automatic translation, visualization). We focus here on the probabilistic models used in these applications, making explicit the link between models and applications, and illustrating it through a presentation of experimental results.