Extracting and evaluating general world knowledge from the Brown Corpus

By Lenhart K. Schubert, Matthew H Tong. 2003

73 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Long Short-Term Memory-Networks for Machine Reading (2016, background): “…et al., 2011), knowledge base population and construction (Ji and Grishman, 2011; Niu et al., 2012; Carlson et al., 2010), ontology learning (Poon and Domingos, 2010b), textual entailment (Dagan et al., 2005), and the creation of proposition stores (Pe˜nas and Hovy, 2010; Schubert and Tong, 2003).”
Commonsense Causal Reasoning Using Millions of Personal Stories (2011, methodology): “Schubert (2002) presented an approach to acquiring Copyright © 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org).”
A study of the knowledge base requirements for passing an elementary science test (2013, background): “…for Passing an Elementary Science Test Peter Clark, Phil Harrison Allen Institute for Artificial Intelligence 505 Fifth Avenue South Seattle, WA 98104 {peterc,philipha}@vulcan.com ABSTRACT Our long-term interest is in machines that contain large amounts of general and scientific…”