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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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Semantic Role Labeling for Process Recognition Questions

Samuel LouvanChetan NaikVeronica Lynnand Peter Clark
2015
K-CAP • First International Workshop on Capturing Scientific Knowledge (SciKnow)

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… 

Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge

Yang Li and Peter Clark
2015
EMNLP

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… 

Exploring Markov Logic Networks for Question Answering

Tushar KhotNiranjan BalasubramanianEric Gribkoffand Oren Etzioni
2015
EMNLP

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… 

VisKE: Visual Knowledge Extraction and Question Answering by Visual Verification of Relation Phrases

Fereshteh SadeghiSantosh Divvalaand Ali Farhadi
2015
CVPR

How can we know whether a statement about our world is valid. For example, given a relationship between a pair of entities e.g., 'eat(horse, hay)', how can we know whether this relationship is true… 

Higher-order Lexical Semantic Models for Non-factoid Answer Reranking

Daniel FriedPeter JansenGustave Hahn-Powelland Peter Clark
2015
TACL

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

Ben HixonPeter Clarkand Hannaneh Hajishirzi
2015
NAACL

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… 

Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering

Rebecca SharpPeter JansenMihai Surdeanuand Peter Clark
2015
NAACL

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… 

Identifying Meaningful Citations

Marco ValenzuelaVu Haand Oren Etzioni
2015
AAAI • Workshop on Scholarly Big Data

We introduce the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort. We believe this task… 

Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!

Peter Clark
2015
Proceedings of IAAI

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… 

Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers

Christopher Clark and Santosh Divvala
2015
AAAI • Workshop on Scholarly Big Data

Identifying and extracting figures and tables along with their captions from scholarly articles is important both as a way of providing tools for article summarization, and as part of larger systems… 

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