Mining Associative Meanings from the Web: from word disambiguation to the global brain

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ABSTRACT . A general problem in all systems to process language (parsing, translating, etc.) is ambiguity: words have many, fuzzily defined meanings, and meanings shift with the context. This may be tackled by quantifying the connotative or associative meaning, which can be represented as a matrix of mutual association strengths. With many thousands of words, there are billions of possible associations, though, and there is no obvious method to measure all of them. This "knowledge acquisition bottleneck" can be tackled by mining implicit associations from the billions of documents and millions of users on the World-Wide Web. The present paper discusses two methods to achieve this: lexical co-occurrence, a measurement of the frequency with which words appear in each other's neighborhood, and web learning algorithms, an application of the Hebbian rule to create associations between subsequently "activated" words or pages. The mechanism of spreading activation can be applied to the resulting associative networks for clustering, contextdriven disambiguation, and personalized recommendation. A generalization of such methods could transform the web into a "global brain", that is, an intelligent, learning network that assimilates the implicit knowledge and preferences of its users. 1.

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    ABSTRACT: We explored the development of sensitivity to causal relations in children’s inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey → predator) or diagnostic (predator → prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children’s inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning.
    Journal of Experimental Child Psychology 06/2014; 122:48–61. DOI:10.1016/j.jecp.2013.11.015 · 3.12 Impact Factor
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    ABSTRACT: Accounts of category-based inductive reasoning can be distinguished by the emphasis they place on structured versus unstructured knowledge. In addition, it has been claimed that certain domains of structured knowledge are more available than others. Using a speeded task paradigm, participants rated the strength of inductive arguments in which the categories were either strongly or weakly associated and shared a taxonomic or causal relation.. Strongly associated categories received higher inductive strength ratings than weakly associated category pairs, regardless of the domain by which the categories were related. Strength of association was highly predictive of inductive strength ratings, but more additional variance was accounted for by beliefs about taxonomic and causal relations when people were not under time pressure. This suggests that, regardless of knowledge domain, maximizing inductive potency relies on the use of both structured and unstructured knowledge, depending on available mental resources.

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Jun 6, 2014