SBIR Phase I: Using Machine Learning and NLP tools to expedite the review and analysis of legal contracts

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是提供一种新的方法来分析和理解结构简洁的文件，如法律合同。这个项目的成功将对法律文件的处理方式带来技术上的颠覆。这将减少处理时间，并在审查法律合同方面节省资金。此外，在法律市场内实现这种程度的颠覆有可能为自动化处理、提高质量和降低影响整个经济的许多其他法律服务的成本打开大门。仅在美国就有4370亿美元的市场规模，法律行业已经准备好迎接技术颠覆，这将使这个市场达到其他行业的水平，这些行业通过采用尖端的机器学习和自然语言处理（NLP）技术得到了显着改善。这凸显了这项正在开发的技术的巨大商业潜力。这个小企业创新研究（SBIR）第一阶段项目追求一种新的创新方法来理解和分析合同，其准确性可与人类所能达到的水平相媲美。无论技术如何，准确性是律师信任和采用这项创新的最重要指标。如果没有解决问题的新方法，标准的NLP技术无法产生期望的精度水平。为该项目提出的方法创建了创新的自定义规则，这些规则使用标准的NLP技术将合同分解为捕获合同含义的不同方面的数据结构，并允许对合同进行更高层次的理解。为了达到期望的精度，设计了一种新的创新递归神经网络（RNN），对提取的契约-意义-数据结构进行学习，显著提高了整个系统的精度。这两个步骤形成了某种正交的学习过程，当与人类监督学习相结合时，可以产生期望的准确性。该项目将通过在足够大的合同语料库上训练核心引擎来全面评估底层技术，从而在更大的范围内测试假设。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a new approach to analyzing and understanding concisely structured documents such as legal contracts. The success of this project would provide a technological disruption in the way legal documents are processed. This will result in reduced processing time and financial savings in the review of legal contracts. Moreover, achieving this level of disruption within the legal market has the potential to open the door to automate processing, improve quality, and reduce the cost of many other legal services impacting the entire economy. With a market size of $437 Billion within the US alone, the legal industry is ready for a technological disruption that would bring this market up to par with other industries that have been significantly improved with the adoption of cutting edge Machine Learning and Natural Language Processing (NLP) technologies. This underlines the massive commercial potential for the technology under development.This Small Business Innovation Research (SBIR) Phase I project pursues a new innovative approach to understanding and analyzing contracts with accuracy comparable to what can be achieved by humans. Regardless of the technology, accuracy is the most important metric for lawyers to trust and adopt this innovation. Standard NLP techniques cannot generate the desired level of accuracy without a new approach to the problem. The approach proposed for this project creates innovative custom-defined rules that operate using standard NLP technologies to break a contract into a data structure capturing different aspects of the contract meaning and allowing a higher level of understanding for the contract. In order to achieve the desired accuracy, a new innovative recurrent neural network (RNN) is designed to learn over the extracted contract-meaning-data-structure which significantly improves the accuracy of the entire system. These two steps form somewhat orthogonal learning processes and when coupled with human-supervised-learning can produce the desired accuracy. This project will allow a full assessment of the underlying technology by training the core engine on a sufficiently large corpus of contracts to test the hypothesis on a larger scale.