{'fraction_non_alphanumeric': 0.018518518518518517, 'fraction_numerical': 0.0, 'mean_word_length': 5.603053435114504, '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}}
Legal Machine-Learning Analysis: First Steps towards A.I. Assisted Legal Research

This research project develops a methodology to utilize machine-learning analysis of live disputes to assist legal professionals in narrowing issues and comparing relevant precedents. Our first step is to extract and classify sentences in breach of contract court decisions according to type. Such court decisions have five basic sentence types: sentences on contract law, sentences on contract holding, sentences on contract issues, sentences on contract reasoning, and sentences on contract facts. The result of this project facilitates further downstream processing such as constructing decision trees that predict the likely outcome for the case at hand, displaying the rationales on which court decisions are based, and calculating the similarity of previous legal precedents.