Special Program Announcement for the Office of Naval Research Research Opportunity Text Analytics for Data to Decisions (D2D) | Apply Online

Opportunity Summary:

The Office of Naval Research (ONR), on behalf of the Office of the Secretary of Defense and ASD(R&E), released this Special Program Announcement to solicit full proposals for the Data to Decisions (D2D) program focused on text analytics. The opportunity sits within ONR C4ISR research areas (Code 31), specifically applied computational analysis, command and control, and intelligent and autonomous capabilities, and it is administered under the broader ONR BAA process (proposals are submitted, evaluated, and awarded as described in ONRBAA13-001). The basic problem the program is trying to solve is straightforward: warfighters and analysts are swimming in text from many more sources than before (open-source reporting, operational reporting, HUMINT, and other unstructured inputs), but staffing is not increasing. D2D is aimed at technologies that convert that growing volume of text into usable, actionable understanding while reducing analyst workload and improving decision speed and quality.

The announcement describes three main research thrust areas that collectively target the pipeline from raw text to operationally relevant insights. The first thrust, Contextual Understanding, is about building richer machine-supported understanding of a region, community, or situation by correlating and aggregating diverse open-source and other available data, even when it is incomplete, contradictory, or uncertain. The program is interested in methods that can help discover events that may have happened or are being planned; identify values, beliefs, and motivations that drive behaviors of interest; detect topics and concepts emerging within communities; map semantic relationships and assess their strength; characterize community structure and clusters within social networks; and perform semantic and trend analysis of emotional support or sentiment toward topics or individuals. A key theme here is fusing heterogeneous indicators (for example, terrain, demographics, economics, agriculture, weather, and social patterns) into context that supports decision-making, often through combined human-machine workflows rather than purely automated outputs.

The second thrust, Event Prediction, targets persistent situational awareness by extracting event information from large volumes of unstructured text and converting it into structured representations (such as databases) that analysts can query and monitor. The centerpiece technical challenge is extracting events along with nuanced attributes that matter for real-world interpretation: modality (whether something is asserted, rumored/believed, hypothetical, or threatened), polarity (did it occur or is it negated), genericity (specific instance versus habitual/general statement), and tense (past, present, future). The solicitation also highlights the need for solutions that can be customized quickly to new text sources, writing styles, formats, and domains. In addition, because defense-oriented text analytics often involves adversarial or covert networks, the program calls for innovative approaches to infer events of interest using proxy indicators and temporal dynamics, such as changes in contact frequency, inter-contact times, recurring interactions, order of communications along paths, and diffusion delays. It further emphasizes scalable social network analysis supported by visual analytics and semantic/ontology-driven methods to reduce complexity, identify key actors and relationships, detect bridging nodes that reveal hidden subnetworks, and characterize flows of resources like information, money, or influence.

The third thrust, Machine Translation and Processing, focuses on the front-end and midstream barriers that keep unstructured, multilingual text from being usable at scale. The announcement notes that while collection and some retrieval capabilities have advanced, automated "ingestion" and understanding have not kept up, especially given the explosion of unstructured content. Since many operational areas involve substantial language and dialect diversity, the program prioritizes computationally efficient approaches for machine translation and downstream processing such as information extraction and automated summarization. It also explicitly includes enabling technologies that bring non-digital sources into the pipeline, such as optical character recognition (OCR) and speech recognition, when those outputs feed translation, extraction, and summarization. Beyond algorithms, ONR indicates interest in building language data resources and evaluation metrics, and in assessing operational impact: how proposed methods change the speed and effectiveness of collection, analysis, and assessment under time constraints. The announcement also encourages the use of open standards as a way to reduce costs and improve interoperability.

From an administrative standpoint, this was a discretionary science and technology R&D opportunity (CFDA 12.300) supporting awards via grants and procurement contracts, with no cost sharing requirement stated in the synopsis. ONR anticipated around 10 awards. The posting date was December 3, 2012, with a full proposal due date of January 15, 2013, and an archive date of February 14, 2013. Eligibility is not fully spelled out in the synopsis and is instead referenced to ONRBAA13-001, meaning applicants needed to consult that BAA for who could apply and for the required proposal format, evaluation approach, and award mechanics. The full announcement was accessed through Grants.gov, and the listed contract specialist contact was Kenesha Hargrave (703-696-5354) for help accessing the announcement.

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