SYSTEM AND METHOD AUTOMATED ANALYSIS OF LEGAL DOCUMENTS WITHIN AND ACROSS SPECIFIC FIELDS
A system for automated analysis of legal documents within and across different fields is constructed using a computer system comprising at least one memory, at least one processor, and at least a first plurality of programming instructions stored in the at least one memory and operating on the at least one processor configured to allow the operation on the computer system of additional programming instructions, an extraction processor to identify, extract knowledge from data contained in the legal document and transform it into a common data form. The analysis processor develop a local and global knowledge graphs containing the key entities, relationships and concepts encoded in the text.
1 . A system for automated analysis of legal documents within and across different fields, comprising:
a computer system comprising at least one memory, at least one processor, and at least a first plurality of programming instructions stored in the at least one memory and operating on the at least one processor configured to allow the operation on the computer system of additional programming instructions;
an extraction processor, comprising at least a second plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the second plurality of programming instructions, when operating on the at least one processor, cause the computer system to:
receive a data set containing legal documents;
perform a set of systematic NLP-based data extraction single-purpose generic micro-functions to identify, extract a set of knowledge data from data contained in the legal document; and
transform the knowledge data into a common data form; and
an analysis processor, comprising at least a second plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the second plurality of programming instructions, when operating on the at least one processor, cause the computer system to:
perform dynamic model selection based on classification algorithms estimating a domain, age, and legal jurisdictions for the knowledge data;
query a catalogue of available models;
generate a knowledge graph from the knowledge data;
perform knowledge graph enrichment and dataset contextualization using legal and domain-specific technical data; and
using knowledge data to provide one or more interpretations for a provision within the knowledge data.
2 . The system of claim 1 , wherein the set of systematic NLP-based data extraction generic micro-functions comprises:
tokenizing a character sequence up into pieces, and throwing away characters, such as punctuation;
transforming all acronyms found in the incoming legal documents into standard set of terms applicable to all of the data regardless of source;
transforming language within the knowledge data to properly use a vocabulary and morphological analysis of words;
identifying references to known people and entities within the documents, regardless of a form of the reference;
recognizing patterns by sentiment and topic extraction, targeted word/sentence clustering, and information retrieval; and
applying system and user defined data transformation and orchestration workflows.
3 . The system of claim 1 , wherein the knowledge graph generator comprises a local graph generator and a global graph generator.
4 . The system of claim 1 , wherein the data set of legal documents includes contracts, court decisions, statutes, court pleadings, and text data.
5 . The system of claim 1 , wherein the extraction processor further comprises a specialty dictionary related to a specific legal subject matter.
6 . A method for automated analysis of legal documents within and across different fields, comprising the steps of:
receiving a data set of legal documents;
identifying and extracting a set of knowledge data from data contained in the legal document;
transforming the knowledge data into a common data form;
selecting a dynamic model used in estimating a domain, age, and legal jurisdictions for the knowledge data;
querying a catalogue of available models;
generating a knowledge graph from the knowledge data;
performing knowledge graph enrichment and dataset contextualization using legal and domain-specific technical data; and
providing one or more interpretations for a provision within the knowledge data.
7 . The method of claim 6 , wherein the method further comprises:
tokenizing a character sequence up into pieces, and throwing away characters, such as punctuation;
transforming all acronyms found in the incoming legal documents into standard set of terms applicable to all of the data regardless of source;
transforming language within the knowledge data to properly use a vocabulary and morphological analysis of words;
identifying references to known people and entities within the documents, regardless of a form of the reference;
performing pattern recognition on the knowledge data including sentiment and topic extraction, targeted word/sentence clustering, and information retrieval; and
applying system and user defined data transformation and orchestration workflows.
8 . The method of claim 7 , wherein the knowledge graph comprises a local knowledge graph and a global knowledge graph.
9 . The method of claim 7 , wherein the data set of legal documents includes contracts, court decisions, statutes, court pleadings, and text data.
10 . The method of claim 7 , wherein the extraction of the set of knowledge data utilizes a specialty dictionary related to a specific legal subject matter.