SBIR Phase II: Building a Flexible, Technology Adaptive Architecture to Support Processing of Content by Knowledge Workers

This Small Business Innovation Research (SBIR) Phase II Project addresses the gap between the capabilities of today's semantic analysis systems and the accuracy requirements of knowledge workers (analysts and researchers) in language-sensitive fields such as public relations, foreign affairs, and crisis management. Knowledge workers in many organizations monitor and analyze print and web coverage for content of interest. When the volume of search results is large, some filter, classify and score the results using products or systems based on semantic analysis technology utilizing extensive libraries of words, patterns, and context-specific algorithms. However, users complain that these systems fall short of desired accuracy, missing rhetorical devices such as irony, sarcasm, metaphors, double entendre, and improperly interpreting connections between sentiment and topics. Users with high thresholds for accuracy thus turn to manual processes to either supplement or substitute for technology. Building upon Phase I work, the company will create and integrate a larger set of content processing modules and enhance a pluggable architecture to support quick insertion and testing of new modules in the content processing "pipeline." Once commercialized, the system will enable more rapid adoption of technology by knowledge workers. In fields with high accuracy requirements, the need for human judgment has constrained technology use to discrete areas like search, while in subsequent processing steps, analysts must manually capture, classify, score, analyze, and report on the output. Feedback to date suggests the product can substantially enhance the productivity and effectiveness of professionals in these fields and that it addresses a number of frustrating gaps in the marketplace.