IARPA BETTER: Multilingual Fine-grained Decompositional Analysis
The amount of unstructured text information generated daily is exponentially increasing. This presents challenges for analysts of various types to classify, triage, and examine all relevant information for their specific problem area of interest, with potential applications to multiple languages. To keep pace with this ever-increasing amount of information, new tools and methods are needed to enable personalized extraction of semantic information from text and the application of such semantic information to triage and retrieval problems.
The BETTER program aims to develop enhanced methods for personalized, multilingual semantic extraction and retrieval from text. The goal is to provide a user with a system that quickly and accurately extracts complex semantic information, targeted for a specific user, from text.
October 2019 - April 2023
Additional (Non-UMD Investigators): Benjamin Van Durme (Johns Hopkins University), Aaron Steven White (University of Rochester), Eugene Agichtein (University of North Carolina, Chapel Hill)
Johns Hopkins University, University of Rochester, University of North Carolina, Chapel Hill
Total Award Amount: $292,586Center and Lab Affiliation: