{'fraction_non_alphanumeric': 0.015625, 'fraction_numerical': 0.006696428571428571, 'mean_word_length': 5.414285714285715, '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}}
Context Encoding LSTM CS 224 N Course Project

This project uses ideas from greedy transition based parsing to build neural network models that can jointly learn to parse sentences and use those parses to guide semantic composition. The model is used for sentence encoding for tasks like Sentiment classification and Entailment. The performance is evaluated on Stanford Sentiment Treebank(SST) and Stanford Natural Language Inference (SNLI) corpus.