Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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Can we derive general world knowledge from texts?
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Novel semantic features for verb sense disambiguation
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Weblogs as a source for extracting general world knowledge
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ASKNet: automated semantic knowledge network
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Evaluation of commonsense knowledge with Mechanical Turk
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Open-domain commonsense reasoning using discourse relations from a corpus of weblog stories
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Evaluating commonsense knowledge with a computer game
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Bootstrapping a Game with a Purpose for Commonsense Collection
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Using natural language to integrate, evaluate, and optimize extracted knowledge bases
Proceedings of the 2013 workshop on Automated knowledge base construction
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We have been developing techniques for extracting general world knowledge from miscellaneous texts by a process of approximate interpretation and abstraction, focusing initially on the Brown corpus. We apply interpretive rules to clausal patterns and patterns of modification, and concurrently abstract general "possibilistic" propositions from the resulting formulas. Two examples are "A person may believe a proposition", and "Children may live with relatives". Our methods currently yield over 117,000 such propositions (of variable quality) for the Brown corpus (more than 2 per sentence). We report here on our efforts to evaluate these results with a judging scheme aimed at determining how many of these propositions pass muster as "reasonable general claims" about the world in the opinion of human judges. We find that nearly 60% of the extracted propositions are favorably judged according to our scheme by any given judge. The percentage unanimously judged to be reasonable claims by multiple judges is lower, but still sufficiently high to suggest that our techniques may be of some use in tackling the long-standing "knowledge acquisition bottleneck" in AI.