Abstract
Human beings inherit an informational culture transmitted through spoken and written language. A growing body of empirical work supports the mutual influence between language and categorization, suggesting that our cognitive-linguistic environment both reflects and shapes our understanding. By implication, artifacts that manifest this cognitive-linguistic environment, such asWikipedia, should represent language structure and conceptual categorization in a way consistent with human behavior. We use this intuition to guide the construction of a computational cognitive model, situated in Wikipedia, that generates semantic association judgments. Our unsupervised model combines information at the language structure and conceptual categorization levels to achieve state of the art correlation with human ratings on semantic association tasks including WordSimilarity-353, semantic feature production norms, word association, and false memory.
Highlights
Miller [1] offered the term informavore to capture our tendencies as cognitive agents to devour the information that we encounter in our environment
At multiple levels of structure, Wikipedia reflects the aspects of meaning that drive semantic associations
Two articles that share inlinks and outlinks are likely to be similar; to the extent that some links may be common across many articles they should be weighted less. This intuition is captured in the Wikipedia Link Measure (WLM) outlink metric, which weights each outlink o by log(|A|/|O|), the log of the number of total articles in Wikipedia |A| divided by the number of articles that link to that page |O|
Summary
Miller [1] offered the term informavore to capture our tendencies as cognitive agents to devour the information that we encounter in our environment. In a related study with 13 month olds, were word labels found to increase attention to novel objects of the same category, but word labels were found to increase attention to the superordinate category (cow–animal), relative to a non-word-label condition [12] These studies demonstrate the mutual influence between language and cognition during development: Word labeling focuses attention on category features, attention to discriminating features improves category structure, and improved category structure facilitates the learning of more word labels. Using Wikipedia as a cognitive-linguistic environment, a computational model that incorporates both the mutual influences of conceptual/categorical organization and the structure of language should produce behavior closer to human behavior than a model without such mutual influence. Our results inform the internalist/externalist debate by showing just how much internal cognitive-linguistic structure used in these tasks is preserved externally in Wikipedia
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