STAR: Semantic Temporal Association Retrieval Navy SBIR FY2006.2 Sol No.: Navy SBIR FY2006.2 Topic No.: N06-153 Topic Title: STAR: Semantic Temporal Association Retrieval Proposal No.: N062-153-0658 Firm: Aptima, Inc. 12 Gill Street Suite 1400 Woburn, Massachusetts 01801 Contact: Andrew Duchon Phone: (781) 496-2490 Web Site: aptima.com Abstract: Intelligence analysts gather and synthesize information from multiple sources in response to both high priority requests and standing topics. Their expertise enables them to evaluate and process information effectively, but they spend too much time searching for relevant information. As ops tempo increases, information overload will only increase. The STAR (Semantic Temporal Association Retrieval) system will support information triage and synthesis and enable analysts to allocate more time to evaluation and genuine analysis. STAR will systematically compare traditional Latent Semantic Analysis and two of its emerging alternatives - Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation - structured around meaningful analyst tasks. We will also combine topic and time-course models to create a tool that automatically compares recent and historical patterns, enhancing triage and increasing the predictive power of intelligence analysis. Both systematic comparison of statistical language processing methods and incorporation of an explicit temporal component are innovations. Benefits: Systematic comparison of statistical language processing methods using realistic tasks will enhance their practical application. Incorporating temporal and linguistic attributes of input documents will extend the scope of statistical language processing methods. Analysts will be able to apply their time and expertise more effectively with enhanced search, retrieval, and analysis. Return