{'fraction_non_alphanumeric': 0.027314112291350532, 'fraction_numerical': 0.019726858877086494, 'mean_word_length': 4.689655172413793, '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}}
Effects of Prediction-Length on Accuracy in Automatic Assamese word prediction

Word prediction is a technique which increases the typing speed of user by suggesting the relevant word that the user is intended to write. In this research n-gram based language models are used to find the prediction accuracy for different size of prediction-list in Assamese language and the testing is done for both in-domain and mixed-domain text-data. Accuracy of prediction is calculated for a prediction-list length from 1 to 10 and the maximum accuracies obtained are 81.39% and 63.81% with prediction-list length 10 for in-domain and mixed-domain text-data respectively.