[The] objective (to use deep learning to wrestle the practically-unknowables down to knowables) seems to be the impetus behind a two-year-old US DoD Advanced Research Projects Agency initiative called Deep Exploration and Filtering of Text (DEFT)… DEFT aims to “analyze textual data at a scale beyond what humans could do by themselves….[DEFT is designed to enable] more efficient…processing [of] text information and…[greater] understanding [of] connections in text that might not be readily apparent to humans….[D]efense analysts [would be able] to efficiently investigate…more documents, which would enable discovery of implicitly expressed, actionable information within those documents.”
DARPA’s ability to deliver on this grand promise is still unproven. However, the range of deep-learning ML approaches included under DEFT is truly impressive. A partial list includes separate functional modules to detect anomalies, disfluency, ambiguity, vagueness, causal relations, person-relations, semantic equivalences, entailments and redundancies in textual corpora.
See on www.ibmbigdatahub.com