{'fraction_non_alphanumeric': 0.019699812382739212, 'fraction_numerical': 0.0, 'mean_word_length': 5.203488372093023, '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}}
Word Activation Forces-Based Language Modeling and Smoothing

N-gram language models are useful for modeling the local dependencies of word occurrences but not for capturing global word dependencies. When the window size n is limited, the n-gram is weak in terms of capturing long distance dependencies. Long-distance Dependency information has long been proven useful in language model. However, the improved performance of long-distance LMs over conventional n-gram models generally comes at the cost of increased decoding complexity and model size. Word Activation Forces has been proven a simple and human-comparable accurate measure to identify word closest associates. In this paper, Word Activation Forces-Based language model is proposed to capture the long distance dependency between words, but which is as fast for decoding as a conventional word n-gram. As shown by experiments on broadcast news, the proposed language modeling and smoothing can significantly reduce the perplexity of language models and word error rate with moderate computational cost.