Department of Defense (DoD) operators and analysts collect and process copious amounts of data from a wide range of sources to create and assess plans and execute missions. However, depending on context, much of the information that could support DoD missions may be implicit rather than explicitly expressed. Having the capability to automatically extract operationally relevant information that is only referenced indirectly would greatly assist analysts in efficiently processing data.
Automated, deep natural-language processing (NLP) technology may hold a solution for more efficiently processing text information and enabling understanding connections in text that might not be readily apparent to humans. DARPA created the Deep Exploration and Filtering of Text (DEFT) program to harness the power of NLP. Sophisticated artificial intelligence of this nature has the potential to enable defense analysts to efficiently investigate orders of magnitude more documents so they can discover implicitly expressed, actionable information contained within them.
By building on the NLP technologies developed in other DARPA programs and ongoing academic research into deep language understanding and artificial intelligence, DEFT aims to address remaining capability gaps related to inference, causal relationships and anomaly detection. Improving human language technology to incorporate these capabilities is essential for enabling automated exposure of important content to facilitate analysis.
As a further aid to analysis, DEFT also aims to enable the capability to integrate individual facts into large domain models as information is processed to support assessment, planning, prediction and the initial stages of report writing. If successful, DEFT will allow analysts to move from limited, linear processing of huge sets of data to a nuanced, strategic exploration of available information.
The development of an automated solution may involve contributions from the linguistics and computer science fields in the areas of artificial intelligence, computational linguistics, machine learning, natural-language understanding, discourse and dialogue analysis, and others.
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