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Identifying Meaningful Citations
Marco Valenzuela, Vu Ha, and Oren EtzioniAAAI • Workshop on Scholarly Big Data • 2015 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 is a crucial component in algorithms that detect and follow…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…Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers
Christopher Clark and Santosh DivvalaAAAI • Workshop on Scholarly Big Data • 2015 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 that seek to gain deeper, semantic understanding of these…Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
Been Kim, Julie Shah, and Finale Doshi-VelezNIPS • 2015 We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to…Parsing Algebraic Word Problems into Equations
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas AngTACL • 2015 This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global…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…VISALOGY: Answering Visual Analogy Questions
Fereshteh Sadeghi, C. Lawrence Zitnick, and Ali FarhadiNIPS • 2015 In this paper, we study the problem of answering visual analogy questions. These questions take the form of image A is to image B as image C is to what. Answering these questions entails discovering the mapping from image A to image B and then extending the…VisKE: Visual Knowledge Extraction and Question Answering by Visual Verification of Relation Phrases
Fereshteh Sadeghi, Santosh Divvala, and Ali FarhadiCVPR • 2015 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 or false in general. Gathering such knowledge about entities…Connotation Frames: A Data-Driven Investigation
Hannah Rashkin, Sameer Singh, Yejin ChoiACL • 2015 Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2…