2 code implementations • 29 Jun 2023 • Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld, Bryan Wilder
However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams.
1 code implementation • 17 Jun 2020 • Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, Tom Mitchell
More precisely, we consider the problem of identifying the unstated presumptions of the speaker that allow the requested action to achieve the desired goal from the given state (perhaps elaborated by making the implicit presumptions explicit).
2 code implementations • EMNLP 2018 • Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William W. Cohen
In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.
Graph Representation Learning Open-Domain Question Answering
1 code implementation • 12 Jul 2017 • Bhuwan Dhingra, Kathryn Mazaitis, William W. Cohen
ClueWeb09 serves as the background corpus for extracting these answers.
no code implementations • 12 Jul 2017 • Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen
Explainable recommendation is an important task.
no code implementations • 10 Jun 2016 • Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, William W. Cohen
We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction.
no code implementations • 12 Apr 2014 • William Yang Wang, Kathryn Mazaitis, Ni Lao, Tom Mitchell, William W. Cohen
We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm.
no code implementations • 10 May 2013 • William Yang Wang, Kathryn Mazaitis, William W. Cohen
In many probabilistic first-order representation systems, inference is performed by "grounding"---i. e., mapping it to a propositional representation, and then performing propositional inference.