Ms. Marjorie Freedman, (PI) a Research Team Leader at ISI, has degrees in linguistics and computer science from Cornell University.
At ISI, Ms. Freedman serves as PI of DARPA’s AIDA and ASED efforts. Under DARPA AIDA, Ms. Freedman’s work has included tailoring speech recognition and optical character recognition systems for use in an information extraction pipeline. Also, under AIDA, she is exploring the impact of uncertainty in anaphora resolution to downstream tasks and working with vision researchers to understand and address the challenges of mapping the output of vision analytics to classic information extraction ontologies. Ms. Freedman has recently joined the advisory board of the University of Colorado’s Professional MS degree in Computational Linguistics, Analytics, Search and Informatics (CLASIC).
Before joining ISI, she served as PI of IAPRA SCIL and Metaphor efforts; and as co-PI of BBN’s DARPA DEFT and LORELEI efforts. As a part of DEFT, she provided guidance in API development and served as the task coordinator for NIST’s TAC 2014-16 Event Argument evaluations. As a part of this evaluation, Ms. Freedman sought to identify a salient unit that could be evaluated and would be useful to downstream knowledge focused tasks. As PI of IARPA SCIL, she developed algorithms to understand the implicit social content of language, for example, identifying persuasive language in online discussion threads. Her work in information extraction has explored how to address limited training data, including fusing rule-based and learned systems, exploring alternative approaches to annotation, and measuring the impact of coreference in bootstrap learning for information extraction.