DICR: AI Assisted, Adaptive Platform for Contract Review

Abstract

In the regular course of business, companies spend a lot of effort reading and interpreting documents, a highly manual process that involves tedious tasks, such as identifying dates and names or locating the presence or absence of certain clauses in a contract. Dealing with natural language is complex and further complicated by the fact that these documents come in various formats (scanned image, digital formats) and have different degrees of internal structure (spreadsheets, invoices, text documents). We present DICR, an end-to-end, modular, and trainable system that automates the mundane aspects of document review and allows humans to perform the validation. The system is able to speed up this work while increasing quality of information extracted, consistency, throughput, and decreasing time to decision. Extracted data can be fed into other downstream applications (from dashboards to Q&A and to report generation).

A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance

“…Precision can be defined as the ratio of the number of answers that are actually correct to the number of answers predicted by GPQAM to be correct, and was calculated using Equation (1). Recall is the ratio of the number of answers predicted to be correct by the GPQAM to the actual number of correct answers, and was calculated using Equation (2). The F1 score is the tradeoff between precision and recall, which is the harmonic mean, that is, (Equation ( 3)).…”

“…A contract is defined as an agreed-upon promise or series of promises between parties for which the law imposes a legal obligation [1]. Checking contract information, such as identifying dates and names in a contract or the presence or absence of specific clauses, is a repetitive and highly manual process [2]. The application of artificial intelligence (AI) in the legal field is not new [3].…”