Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach
Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over…
Selecting Near-Optimal Learners via Incremental Data Allocation
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give…
Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge
Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears…
BDD-Guided Clause Generation
Nogood learning is a critical component of Boolean satisfiability (SAT) solvers, and increasingly popular in the context of integer programming and constraint programming. We present a generic…
Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!
While there has been an explosion of impressive, datadriven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond…
Exploring Markov Logic Networks for Question Answering
Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from…
Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models…
Learning Knowledge Graphs for Question Answering through Conversational Dialog
We describe how a question-answering system can learn about its domain from conversational dialogs. Our system learns to relate concepts in science questions to propositions in a fact corpus, stores…
Semantic Role Labeling for Process Recognition Questions
We consider a 4th grade level question answering task. We focus on a subset involving recognizing instances of physical, biological, and other natural processes. Many processes involve similar…
Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering
Monolingual alignment models have been shown to boost the performance of question answering systems by "bridging the lexical chasm" between questions and answers. The main limitation of these…