Recognizing Event Attributes from Unstructured Text (REACT) Phase II

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-18-C-0088
Agency Tracking Number: D2-2122
Amount: $1,499,903.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: OSD12-LD5
Solicitation Number: 2012.3
Timeline
Solicitation Year: 2012
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-08-29
Award End Date (Contract End Date): 2020-11-30
Small Business Information
2435 N. Central Expressway, Richardson, TX, 75080
DUNS: 127802234
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Sean Monahan
 (972) 231-0052
 smonahan@languagecomputer.com
Business Contact
 Dr. Finley Lcatusu
Phone: (972) 310-0052
Email: finley@languagecomputer.com
Research Institution
N/A
Abstract
A significant challenge in extracting event attributes from unstructured text is that events possess a wide array of attributes which affect decision making. In a single document, an author may describe events that have happened (realis), did not happen or could have happened (irrealis), and that may happen in the future. They may describe events which reflect a generic class of occurrences, or a specific event at a known place and time. Author use of event attributes can also be used to signal group dynamics, motivations, and relationships. In this DARPA Phase II SBIR effort we seek to develop a novel system for understanding, extracting, and conveying events and their relevant event attribute information to analysts and software tools to facilitate their complete understanding of the events within documents of interest. We propose to build on our prototype developed previously under this SBIR (for AFRL) with the goal of extending the state-of-the-art in both the quality of the event attributes extracted, as well as in the types of attributes which can be extracted. Our goal will be to not just extract these attributes, but to understand how the attributes interact and how this information can best be searched and conveyed to analysts. The attributes extracted include the genericity (specific/generic), the realis/irrealis, and the factuality of the events. We will utilize a variety of models, including Long Short-Term Memory (LSTM), self-attention networks, and probabilistic inference. In the proposed option, we will utilize linguistic signals to estimate when future events might occur, and to analyze group dynamics based on author use of event attributes.

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

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