{'fraction_non_alphanumeric': 0.01897810218978102, 'fraction_numerical': 0.0, 'mean_word_length': 5.18018018018018, '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}}
An Adaptive Algor ithm for Learning Changes in Run-Time Context Domain

The run-time context domain has much effect on the performance of practical corpus-based applications. Previous smoothing techniques, and class-based and similarity-based models cannot handle the dynamic status perfectly. In this paper, an adaptive learning algorithm is proposed for task adaptation to fit best the run-time context domain in the application of Chinese homophone disambiguation. It shows which objects to be adjusted and how to adjust their probabilities by a neural network model. The experimental results demonstrate the effects of the learning algorithm from generic domain to specific domain.