Building a Named Entity Recognition Model for the Legal Domain
We defined NER in the legal domain and presented our approach towards generating ground truth data. In what follows, we go over the state-of-the-art in the NER domain and elaborate on the experiments we ran and the lessons we learned.
Named Entity Recognition in the Legal Domain
Named entity recognition is a difficult challenge to solve, particularly in the legal domain. Extracting ground truth labels from long, hierarchical documents is often slow and prone to error. RelationalAI proposes a new scalable algorithm based on the principles of data-centric AI, designed to meet this challenge and generate high-quality annotations with minimal supervision.
What's So Special About Graph Analytics?
Graph analytics help us make sense of our connected data by understanding the structure of our data. They help us see which patterns are important and which aren't. They help us predict what’s coming next. And they help us find control points so that we can be prescriptive and enact change.
Parsing the Crowded World of Data Analytics: Highlights
Our board member Bob Muglia recently met with Sanjeev Mohan in an interview for the It Depends podcast. Bob and Sanjeev discussed the challenges, trends, technologies and the general pulse of the ever-changing data analytics market. Here are some highlights from their discussion!
RelationalAI CLI and Public GitHub Repo
We are excited to announce the RelationalAI Command-Line Interface, which is used to interact with the Relational Knowledge Graph Management System (RKGMS).
Knowledge Graphs for Earthquake Data
Rel and the Relational Knowledge Graph System provide an excellent tool for investigating and analyzing seismic data. This project illustrates an example of working with data that is distributed geographically and temporally.