PERFECTA: Politeness-based Evaluation of Regard for Finding Efforts to Create Trust and Affiliation

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-17-C-0066
Agency Tracking Number: D2-1736
Amount: $1,482,577.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: SB041-009
Solicitation Number: 2004.1
Solicitation Year: 2004
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-03-29
Award End Date (Contract End Date): 2019-06-29
Small Business Information
319 1st Ave North, Minneapolis, MN, 55401
DUNS: 103477993
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Christopher Miller
 Chief Scientist
 (612) 716-4015
Business Contact
 Ms. Linda Holje
Phone: (612) 226-5061
Research Institution
SIFT will identify a set of linguistic events or strategies, occurring in on-line discourse and dialogues (such as email, tweets, blog discussions, etc.) that are expected to alter trust, affiliation, and/or regard on the recipients.The Brown and Levinson model suggests several of these such as "bullying", "buttering up", "discrediting", etc. A politeness-based linguistic analysis approach, augmented as needed with deep semantic processing and plan-recognition techniques, will be combined to provide recognizers for such strategies.Candidate strategies will be derived from a literature review of fields such as the psychology of team and group affiliation, marketing and communication, and from interaction with SMEs in the detection of social influence.Recognizers will be validated first through laboratory experiments establishing correlations with human rating of handcrafted dialogues and then to dataset(s) of real discourse data in the modalities of interest.Concurrently, if suitable datasets can be found or created (such as in rating data for products offered or changed after discourse), SIFT will explore the real, behavioral effects of these strategies.Because the Brown and Levinson model is culturally universal, SIFT will also illustrate transitioning our approach to recognize influence strategies across discourse domains, interaction modes and/or cultures.

* Information listed above is at the time of submission. *

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