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Agreement Natural Language

As its SaaS offering grew on AWS and even had the ability to quickly retrieve contract data in a cloud environment, the LinkSquares team still needed a scalable solution to identify and classify legal language. Initially, they set up a contract database, but as the company grew and needed a more complex and demanding approach, it also needed an automated solution to extract important contractual metadata. LinkSquares turned to SFL Scientific to develop a solution to algorithmically extract keywords from different text documents. For many companies, harnessing the full capacity and effectiveness of artificial intelligence begins with research and the development of an effective roadmap. LinkSquares attempts to disrupt the legal and financial sector by providing an automated software-based solution to optimize contract analysis. The company quickly expanded and had to optimize its existing solution to increase its services. Since AI itself can create new offerings, products and increase the accuracy and efficiency of services, we plan to provide machine learning and natural language processing on text documents. Agreement in Natural Language is a collection of fifteen contributions on the theme of grammatical agreement. The work of renowned linguists constitutes theoretical, descriptible, functional, historical and coherence-developing approaches. They are the result of presentations at an international conference at Stanford University, supported by the Department of Linguistics and the Center for the Study of Language and Information. Although the work has very different approaches, it all refers to intensive research, either on general theoretical questions or on description problems. In recent years, the quality of sample recognition in unstructured data has improved considerably. The reason is, besides the hardware improvements, especially a group of algorithms that go under the name of neural networks or deep learning.

One of the key features of these approaches is that with sufficient training data, they form their own rules to achieve a specific goal. This allows the definition of millions of implicit rules in order to successfully identify fairly complex patterns. In our experience, projects can only be designed by combining in-house know-how and natural language processing. Contact us to ask if we think your project is technically feasible. To really understand how a particular layout works, and therefore a whole document, it is necessary to understand the actual language in that sense. It is known, however, that legal language has a distinct use and style, known as „legalese“, largely characterized by its obscuration towards readers who are not experts in the field. This is therefore a particular challenge for traditional general-purpose NLP solutions, as they are very different from the typical English from the English angle. Consider the following provision, a very exceptional definition of an „addition termination event“ clause, which is included in an ISDA-Master contract. This is an additional redundancy event, with respect to Part A, if it ceases at any time and for any reason , (directly or indirectly) at least fifty-one per cent (51%) to be in possession. (on a fully diluted basis) of the capital stock of each category in Part B.“ While there are 52 words in legalese, if done in standard English, it only takes 23: „If Party A does not own 51% of the total stock of Part B, then an additional redundancy event occurs compared to Part A.“



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