138 citations · 243 across the 8 of their papers we have counts for
9 papers
Computationally Assisted Quality Control for Public Health Data Streams
Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld +1
Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the mo…
Conversational Neuro-Symbolic Commonsense Reasoning
Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki +3
In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated pre…
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer +3
Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answ…
Quasar: Datasets for Question Answering by Search and Reading
Bhuwan Dhingra, Kathryn Mazaitis, William W. Cohen
We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S…
TensorLog: Deep Learning Meets Probabilistic DBs
William W. Cohen, Fan Yang, Kathryn Rivard Mazaitis
We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neural-netwo…
Explainable Entity-based Recommendations with Knowledge Graphs
Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi +1
Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is una…
Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora
Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis +2
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…
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic
William Yang Wang, Kathryn Mazaitis, Ni Lao +2
One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information…
Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic
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 propositio…