{'fraction_non_alphanumeric': 0.018322762508809022, 'fraction_numerical': 0.007751937984496124, 'mean_word_length': 5.367713004484305, '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}}
Mining Contracts for Business Events and Temporal Constraints in Service Engagements

Contracts are legally binding descriptions of business service engagements. In particular, we consider business events as elements of a service engagement. Business events such as purchase, delivery, bill payment, and bank interest accrual not only correspond to essential processes but are also inherently temporally constrained. Identifying and understanding the events and their temporal relationships can help a business partner determine what to deliver and what to expect from others as it participates in the service engagement specified by a contract. However, contracts are expressed in unstructured text and their insights are buried therein. Our contributions are threefold. We develop a novel approach employing a hybrid of surface patterns, parsing, and classification to extract 1) business events and 2) their temporal constraints from contract text. We use topic modeling to 3) automatically organize the event terms into clusters. An evaluation on a real-life contract dataset demonstrates the viability and promise of our hybrid approach, yielding an F-measure of 0.89 in event extraction and 0.90 in temporal constraints extraction. The topic model yields event term clusters with an average match of 85 percent between two independent human annotations and an expert-assigned set of class labels for the clusters.