{'fraction_non_alphanumeric': 0.019230769230769232, 'fraction_numerical': 0.0029585798816568047, 'mean_word_length': 5.637254901960785, '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}}
Improvement of non-negative matrix factorization based language model using exponential models

This paper describes the use of exponential models to improve non-negative matrix factorization (NMF) based topic language models for automatic speech recognition. This modeling technique borrows the basic idea from latent semantic analysis (LSA), which is typically used in information retrieval. An improvement was achieved when exponential models were used to estimate the a posteriori topic probabilities for an observed history. This method improved the perplexity of the NMF model, resulting in a 24% perplexity improvement overall when compared to a trigram language model.