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Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach
Shuo Yang, Tushar Khot, Kristian Kersting, and Sriraam NatarajanAAAI • 2016 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 continuous time. Existing approaches, however, have focused on…Selecting Near-Optimal Learners via Incremental Data Allocation
Ashish Sabharwal, Horst Samulowitz, and Gerald TesauroAAAI • 2016 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 near-optimal accuracy when trained on all data, while also…Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge
Yang Li and Peter ClarkEMNLP • 2015 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 to convey. The scene constitutes an understanding of the text…BDD-Guided Clause Generation
Brian Kell, Ashish Sabharwal, and Willem-Jan van HoeveCPAIOR • 2015 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 method to learn valid clauses from exact or approximate binary…Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!
Peter ClarkProceedings of IAAI • 2015 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 information explicitly stated in text, and to explain and discuss…Exploring Markov Logic Networks for Question Answering
Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, and Oren EtzioniEMNLP • 2015 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 textbooks, represented in a subset of first-order logic. Automatic…Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu, and Peter ClarkTACL • 2015 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 acquire term alignment probabilities from semistructured data…Learning Knowledge Graphs for Question Answering through Conversational Dialog
Ben Hixon, Peter Clark, and Hannaneh HajishirziNAACL • 2015 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 new concepts and relations in a knowledge graph (KG), and…Semantic Role Labeling for Process Recognition Questions
Samuel Louvan, Chetan Naik, Veronica Lynn, Ankit Arun, Niranjan Balasubramanian, and Peter ClarkK-CAP • First International Workshop on Capturing Scientific Knowledge (SciKnow) • 2015 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 entities and are hard to distinguish using simple bag-of-words…Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering
Rebecca Sharp, Peter Jansen, Mihai Surdeanu, and Peter ClarkNAACL • 2015 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 approaches is that they require semistructured training data in the…