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Learning Everything about Anything: Webly-Supervised Visual Concept Learning
Santosh K. Divvala, Ali Farhadi, and Carlos GuestrinCVPR • 2014 Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that…Learning to Solve Arithmetic Word Problems with Verb Categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate KushmanEMNLP • 2014 This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an…Modeling Biological Processes for Reading Comprehension
Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Brad Huang, Christopher D. Manning, Abby Vander Linden, Brittany Harding, and Peter ClarkEMNLP • 2014 Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single…Open Question Answering Over Curated and Extracted Knowledge Bases
Anthony Fader, Luke Zettlemoyer, and Oren EtzioniKDD • 2014 We consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this paper, we present oqa, the…Diagram Understanding in Geometry Questions
Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, and Oren EtzioniAAAI • 2014 Automatically solving geometry questions is a longstanding AI problem. A geometry question typically includes a textual description accompanied by a diagram. The first step in solving geometry questions is diagram understanding, which consists of identifying…Discourse Complements Lexical Semantics for Non-factoid Answer Reranking
Peter Jansen, Mihai Surdeanu, and Peter ClarkACL • 2014 We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on…A Lightweight and High Performance Monolingual Word Aligner
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkACL • 2013 Fast alignment is essential for many natural language tasks. But in the setting of monolingual alignment, previous work has not been able to align more than one sentence pair per second. We describe a discriminatively trained monolingual word aligner that…Automatic Coupling of Answer Extraction and Information Retrieval
Xuchen Yao, Benjamin Van Durme, and Peter ClarkACL • 2013 Information Retrieval (IR) and Answer Extraction are often designed as isolated or loosely connected components in Question Answering (QA), with repeated overengineering on IR, and not necessarily performance gain for QA. We propose to tightly integrate them…Answer Extraction as Sequence Tagging with Tree Edit Distance
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkNAACL • 2013 Our goal is to extract answers from preretrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer…A Study of the Knowledge Base Requirements for Passing an Elementary Science Test
Peter Clark, Phil Harrison, and Niranjan BalasubramanianCIKM • AKBC • 2013 Our long-term interest is in machines that contain large amounts of general and scientific knowledge, stored in a "computable" form that supports reasoning and explanation. As a medium-term focus for this, our goal is to have the computer pass a fourth-grade…