2013 - IBM Research Outstanding Technical Accomplishment Award
2013 - IBM Research A-Level Accomplishment Award
2010 - IBM Research A-Level Accomplishment Award
2008 - IBM Research A-Level Accomplishment Award
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- Alon Halevy used SystemT as a hands-on component of a Ph.D course on Data on the Web at the University of Aalborg in Denmark. Alon said: "The tutorial did a great job giving the students the feeling for the challenges involved in extracting structured data from text." - Nov 2015 [link to class]
- We gave a tutorial on Transparent Machine Learning for Information Extraction at EMNLP 2015 on Sept. 17 [link] [slides in pdf]
- We demoed VINERy, the latest SystemT Web Tooling at VLDB 2015 on Sept. 2 -3 [video] [link]
Information extraction (IE) refers to the task of extracting structured information from unstructured or semi-structured data. In recent years, IE has become increasingly important to a wide array of enterprise applications, ranging from Business Intelligence to Data-as-a-Service. Such applications drive the following main requirements for IE systems: accuracy, productivity, scalability, expressiviity, transparency, and customizability.
SystemT, a declarative IE system, has been designed and developed to address these requirements. It is based on the basic principle underlying relational database technology: complete separation of specification from execution. SystemT uses a declarative rule language, AQL, and an optimizer that generates high-performance algebraic execution plans for AQL rules. It makes IE orders of magnitude more scalable and easy to use, maintain and customize.
SystemT ships today with multiple products across 4 IBM Software Brands. Furthermore, SystemT is used in multiple ongoing research projects and being taught in universities. Our ongoing research and development efforts focus on making SystemT more usable for both technical and business users, and continuing enhancing its core functionalities based on natural language processing, machine learning, and database technology.