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Eric Nyberg

Office: 6715 Gates & Hillman Centers

Email: ehn@cs.cmu.edu

Phone: 412-268-7281

Fax: 412-268-7281

Department(s):
Language Technologies Institute

Personal Website

Eric Nyberg

Professor

Research Interests:

Noted for his contributions to the fields of automatic text translation, information retrieval, and automatic question answering, Eric Nyberg received his Ph.D. from Carnegie Mellon University (1992), and his B.A. from Boston University (1983). In 2012, Nyberg received the Allen Newell Award for Research Excellence for his scientific contributions to the field of question answering and his work as an original developer on the Watson project. He received the BU Computer Science Distinguished Alumna/Alumnus Award on September 27, 2013.

Current Projects

The LiveQA Challenge. Beginning in 2015, Nyberg has collaborated with Yahoo! Labs (as part of the InMind project) to develop automatic answering agents that can respond to real-time questions from web users (like those received by the Yahoo! Answers community QA web site). CMU student Di Wang created a LiveQA system which combined standard retrieval algorithms (BM25) with state-of-the-art deep learning models to achieve the highest score among all participants in the 2015 TREC LiveQA Challenge. In 2016, Di extended his system to include a novel answer ranking method based on attentional encoder-decoder recurrent neural networks and achieved the highest score among 25 automatic systems that were evaluated in the 2016 LiveQA Track.

The BioASQ Challenge. From 2012 to 2016, a team led by Nyberg and LTI Ph.D. student Zi Yang collaborated with Hoffman-LaRoche's Innovation Center to develop information systems for unstructured biomedical text, including a passage retrieval system for the TREC Genomics dataset, a decision support system for gene targeting which leverages information gathered from PubMed articles by an automatic QA system, a Biomedical Semantic QA system which received six 1st-place scores in the 2015 BioASQ Challenge tasks, which included snippet retrieval, concept retrieval, and exact answer retrieval, and a Biomedical Semantic QA system which received three 1st-place scores in exact answer retrieval in the 2016 BioASQ Challenge. See case studies.