Improving Personal Name Search in the TIGR System

This paper describes the development and evaluation of enhancements to the specialized information retrieval capabilities of a multimodal reporting system. The system enables collection and dissemination of information through a distributed data architecture by allowing users to input free text documents, which are indexed for subsequent search and retrieval by other users. This unstructured data entry method is essential for users of this system, but it requires an intelligent support system for processing queries against the data. The system, known as TIGR (Tactical Ground Reporting), allows keyword searching and geospatial filtering of results, but lacked the ability to efficiently index and search person names and perform approximate name matching. To improve TIGR’s ability to provide accurate, comprehensive results for queries on person names we iteratively updated existing entity extraction and name matching technologies to better align with the TIGR use case. We evaluated each version of the entity extraction and name matching components to find the optimal configuration for the TIGR context, and combined those pieces into a named entity extraction, indexing, and search module that integrates with the current TIGR system. By comparing system-level evaluations of the original and updated TIGR search processes, we show that our enhancements to personal name search significantly improved the performance of the overall information retrieval capabilities of the TIGR system.