Data Engineer, PhD in Computer Science at University of Seville. His thesis is about NLP. Specifically, he developed several Deep Learning Models for the identification of conditional clauses in user opinions about different products and services. In addition, he developed a scalable system based on microservices to integrate these models for the identification of conditional clauses with sentiment analysis based on aspects. Previously, he worked on different projects at the University of Seville and Dinamic Area, a company in which he cofounded and led the development of Opileak, a product based on the analysis of social network services.
Recently, there is a growing interest in applying AI in sectors that traditionally have been reluctant to use technology. The legal sector is one of them. Machine learning approaches are used to improve the work of entry-level lawyers. One application consists on extracting relevant information from the tones of documents that law firms possess for a case. In this talk, we are going to present a way to process unstructured data by means of Azure Cognitive Search and Databricks/MLFlow in order to extract that information to the lawyers. Another application relies on a solution for a class action case in which the law firm requires to select lead cases in order to represent the whole set of claimants in courts. This kind of cases consists on hundreds, or even thousands, of claimants. Different optimization approaches can be used for this case. We are going to talk about the one that we followed and implemented in Databricks/MLFlow. In summary, different uses of the AI are presented to help legal sector in its modernization.