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. Hello Yogesh, From what I can say, it`s a combination of machine learning (for example.B. Vector machines specify support to classify clause language into certain types of clauses) AND the modeling of knowledge specific to ontology with respect to certain contractual areas (e.g.B. option contracts) and more generally.
Different startups (and some well-established players) work on these ontological dataframeworks as well as analytics tools. We haven`t made any comparative repositories, but we see the most Seal software in corporate deliveries, but there are tools from other companies like Kira, Ravn, Recommind (Opentext), eBrevia, LegalRobot, Counselytics and others (who will probably comment on this blog post!). If you want to check out a cool provider (and site) and see a good dump summary of all things legal tech, go to legalese.com/v1.0/page/present also look at the ArtificalLawyer blog – it`s excellent. Thank you so much for writing in. AI has many cases of legal application, from document search to compliance and cancellation of the contract. This week, we discuss with Lars Mahler, Chief Science Officer for LegalSifter, what can be done today with AI for legal services and how AI applications work for legal teams, such as .B process-based natural language contract analysis. In addition, Mahler discusses how corporate lawyers and data scientists work together to train machine learning algorithms. A financial services company can be represented as the sum of its legal agreements. A complete understanding of an entity`s contractual obligations in its exchange of equity, debt and liquidity transactions, including a company`s regulatory roles, responsibilities and obligations, is essential to the success of each business.