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Connecting the dots between news articles
TLDR
This paper investigates methods for automatically connecting the dots -- providing a structured, easy way to navigate within a new topic and discover hidden connections in the news domain.
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Trains of thought: generating information maps
TLDR
This work proposes a methodology for creating structured summaries of information, which is able to produce maps which help users acquire knowledge efficiently and integrates user interaction into the framework, allowing users to alter the maps to better reflect their interests.
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Inside Jokes: Identifying Humorous Cartoon Captions
TLDR
This work describes how judgments about the humorousness of different captions are acquired and builds a classifier to identify funnier captions automatically, and uses it to find the best captions and study how its predictions could be used to significantly reduce the load on the cartoon contest's judges.
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Learning to Route
TLDR
The preliminary results regarding the power of data-driven routing suggest that applying ML (specifically, deep reinforcement learning) to this context yields high performance and is a promising direction for further research.
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Connecting Two (or Less) Dots: Discovering Structure in News Articles
TLDR
This article focuses on the news domain: given two news articles, a system automatically finds a coherent chain linking them together, and provides a fast search-driven algorithm to connect two fixed endpoints.
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SOLVENT: A Mixed Initiative System for Finding Analogies between Research Papers
TLDR
SOLVENT is introduced, a mixed-initiative system where humans annotate aspects of research papers that denote their background, purpose, mechanism, and findings, and a computational model constructs a semantic representation from these annotations that can be used to find analogies among the research papers.
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Towards a Theory of AI Completeness
  • Dafna ShahafEyal Amir
  • Computer Science, Mathematics
    AAAI Spring Symposium: Logical Formalizations of…
  • 2007
TLDR
This work serves as a formal basis for investigation of problems that researchers treat as hard AI problems and allows progress in AI as a field to be more measurable, instead of measurable with respect to problem-specific quantities.
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Turning down the noise in the blogosphere
TLDR
This work defines the problem of learning a personalized coverage function by providing an appropriate user-interaction model and formalizing an online learning framework for this task, and provides a no-regret algorithm which can quickly learn a user's preferences from limited feedback.
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Information cartography: creating zoomable, large-scale maps of information
TLDR
Pilot user studies over real-world datasets demonstrate that the proposed methodology for creating structured summaries of information, called zoomable metro maps, helps users comprehend complex stories better than prior work.
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Tractable near-optimal policies for crawling
TLDR
A tractable algorithm is presented that provides a near-optimal solution to the crawling problem, a fundamental challenge at the heart of web search, and it is shown that the optimal randomized policy can be found exactly in O(n⁡log⁡n) operations.
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