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，如法律的合同。该项目的成功将在处理法律的文件的方式上提供技术上的突破。这将减少审查法律的合同的处理时间和节省资金。此外，在法律的市场内实现这种程度的颠覆，有可能为自动化处理打开大门，提高质量，并降低影响整个经济的许多其他法律的服务的成本。仅在美国就有4370亿美元的市场规模，法律的行业已经准备好迎接技术的颠覆，这将使这个市场与其他通过采用尖端机器学习和自然语言处理（NLP）技术而得到显着改善的行业相提并论。这项小型企业创新研究（SBIR）第一阶段项目追求一种新的创新方法，以与人类可以实现的准确性相媲美的方式来理解和分析合同。无论哪种技术，准确性都是律师信任和采用这一创新的最重要指标。如果没有新的方法来解决这个问题，标准的NLP技术就无法产生所需的准确度。 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技术将合同分解为一个数据结构，捕获合同含义的不同方面，并允许对合同进行更高层次的理解。为了达到所需的准确性，一个新的创新的递归神经网络（RNN）的设计，学习提取的合同意义的数据结构，显着提高了整个系统的准确性。这两个步骤形成了某种程度上正交的学习过程，当与人类监督学习相结合时，可以产生所需的准确性。该项目将允许通过在足够大的合同语料库上训练核心引擎来全面评估基础技术，以便在更大范围内测试假设。

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.