Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet

  • Authors:

  • Vera Sheinman;Christiane Fellbaum;Isaac Julien;Peter Schulam;Takenobu Tokunaga

  • Affiliations:

  • Computer Science Department, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan 152-8552;Computer Science Department, Princeton University, Princeton, USA 08540;Computer Science Department, Princeton University, Princeton, USA 08540;Computer Science Department, Princeton University, Princeton, USA 08540;Computer Science Department, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan 152-8552

  • Venue:
  • Language Resources and Evaluation
  • Year:
  • 2013

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Abstract

We propose a new semantic relation for gradable adjectives in WordNet, which enriches the present, vague, similar relation with information on the degree or intensity with which different adjectives express a shared attribute. Using lexical-semantic patterns, we mine the Web for evidence of the relative strength of adjectives like "large", "huge" and "gigantic" with respect to their attribute ("size"). The pairwise orderings we derive allow us to construct scales on which the adjectives are located. To represent the intensity relation among gradable adjectives in WordNet, we combine ordered scales with the current WordNet dumbbells based on the relation between a pair of central adjectives and a group of undifferentiated semantically similar adjectives. A new intensity relation links the adjectives in the dumbbells and their concurrent representation on scales. Besides capturing the semantics of gradable adjectives in a way that is both intuitively clear as well as consistent with corpus data, the introduction of an intensity relation would potentially result in several specific benefits for NLP.