Empirical Methods in an Open Domain Question Answering System

1 online resource (37 pages) : PDF

2015

University of North Carolina at Charlotte

Answering questions effectively involves a complex interconnected set of linguistic and statistical analysis tools, which can be difficult to investigate and evaluate on their own. To evaluate any one of them, I developed two composable, recursive linguistic annotation pipelines and many segments associated therewith in order to facilitate the generation, transformation, analysis and ranking of answers to open domain English questions. The resulting pipeline is to the author’s knowledge the first open-source analog to the Watson system, and has reached 41% precision in the first rank when answering trivia questions from Jeopardy!.

masters theses

Computer science

M.S.

Computational Linguistics
Machine Learning
Natural Language Processing
Question Answering

Computer Science

Zadronzy, Wlodek

Ras, Zbigniew
Cukic, Bojan

Thesis (M.S.)--University of North Carolina at Charlotte, 2015.

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Gallagher_uncc_0694N_11010

http://hdl.handle.net/20.500.13093/etd:517