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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

Group Name


We are hiring! Multiple positions available. Email your resume to Laura Chiticariu (first 5 letters of last name {at}



  • State-of-the-art AQL language for expressing NLP algorithms, optimizer and runtime engine for execution at scale, and easy to use user interface (see a demo)
  • Publications in top NLP, database systems, hardware and HCI conferences
  • Currently taught in multiple universities
  • Winner of multiple IBM Corporate Awards for its contributions to IBM products and clients


Recent events [more news]

  • We will present our semi-automatic approach for generating propositional banks for low-resource languages at EMNLP 2016
  • We are demonstrating our multilingual Semantic Role Labeler at ACL 2016 [video] [paper]
  • We posted an invited expert article on [link]
  • We gave a talk on SystemT in MIT CSAIL on March 8, 2016 [link]
  • Shimei Pan taught SystemT in University of Maryland, Baltimore County, in Spring 2016. Shimei said: "The Information Extraction with SystemT class, which was offered for the first time at UMBC, has been a wonderful experience for me and my students.  I really enjoyed teaching this course. Students were also very enthusiastic. Based on the feedback from my students, they have learned a lot. Some of my students even want me to offer this class every semester."


More about SystemT

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.