Rajasekar Krishnamurthy: Document Intelligence for Enterprise AI Applications: Requirements & Research Challenges

Abstract: Enterprise applications and Business processes rely heavily on experts and knowledge workers reading, searching and analyzing business documents to perform their daily tasks. For instance, legal professionals read contracts to identify non-standard clauses, risks and exposures. Loan officers analyze borrower business documents to understand income, expense and contractual commitments before making lending decisions. Document Intelligence is the ability for a system to read, understand and interpret business documents through the application of AI-based technologies. It has the potential to significantly improve an employee's productivity and an organization's effectiveness by augmenting the expert in their daily task. Several challenges arise in this context such as variability in document authoring, necessity to contextually understand textual and tabular content and organization/role-specific variations in semantic interpretations. Furthermore, as experts rely on document intelligence, they expect the system to exhibit key properties such as explainability, consistent model evolution and ability to enhance the system's knowledge with a few examples. In this talk, using real-world enterprise application examples, I first describe how document intelligence can play a key role in augmenting enterprise AI applications. I then outline key challenges that arise in business document understanding and desiderata that enterprise AI applications and users expect. I conclude with a set of open research challenges that need to be tackled spanning across language understanding, knowledge representation and reasoning, deep learning and systems research. Biography: Rajasekar Krishnamurthy is a Principal Research Staff Member and Senior Manager leading the Watson Discovery team in the Watson AI organization. Prior to this role, he was a Principal Research Staff Member at IBM Research - Almaden leading the NLP, Entity Resolution and Discovery department. Rajasekar's technical interests focus around helping enterprises derive business insights from a variety of unstructured content sources ranging from public and third-party data sources to governing business documents within an enterprise. Rajasekar has expertise in building scalable and usable analytics tools for individual stages in analyzing unstructured documents, such as text analytics, document structure analysis and entity resolution. He is a member of the IBM Academy of Technology. He received a B.Tech in Computer Science and Engineering from the Indian Institute of Technology-Madras, and a Ph.D.in Computer Science from the University of Wisconsin-Madison.