Extracting Meaningful Entities from Human-generated Tactical Reports

Military intelligence analysts use automated tools to exploit physics-based sensor data to construct a spatio-temporal picture of adversary entities, networks, and behaviors on the battlefield. Traditionally, tools did not exploit human generated, textual reports, leaving analysts to manually map dots on the map into meaningful entities using background knowledge about adversary equipment, organization, and activity. Current off-the-shelf text extraction techniques underperform on tactical reports due to unique characteristics of the text. Tactical reports typically feature short sentences with simple grammar, but also tend to include jargon and abbreviations, do not follow grammatical rules, and are likely to have spelling errors. Likewise, named entity recognizers have low recall, because few of the names in reports appear in standard dictionaries. We have developed an entity extraction capability tailored to these challenges, and to the specific needs of analysts, as part of a comprehensive exploitation and fusion system. With fewer cues from syntax, our approach uses semantic constraints to disambiguate syntactic patterns, implemented by a hybrid system that post-processes the output from a standard Natural Language Processing (NLP) engine with our custom semantic pattern analysis. Additional functionality extracts military time and location formats – essential elements that enable downstream fusion of extracted entities with sensor information resulting in a compact and meaningful representation of the battlefield situation.