Kind - The Taxonomy Project

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We designed a statistically-based taxonomy induction algorithm consisting of a combination of different strategies not involving explicit linguistic knowledge. Being all quantitative, the strategies we present are however of different nature. Some of them are based on the computation of distributional similarity coefficients which identify pairs of sibling words or co-hyponyms, while others are based on asymmetric co-occurrence and identify pairs of parent-child words or hypernym-hyponym relations. A decision making process is then applied to combine the results of the previous steps, and finally connect lexical units to a basic structure containing the most general categories of the language. We are currently in the process of evaluating the latest results.


This project has been supported by two successive grants:

  1. Conicyt-Fondecyt 11140686, “Inducción automática de taxonomías de sustantivos generales y especializados a partir de corpus textuales desde el enfoque de la lingüística cuantitativa” (Automatic taxonomy induction from corpora for terminology and general vocabulary using quantitative measures). Lead researcher: Rogelio Nazar. (2014 to 2017).
  2. Ecos Sud-Conicyt Project C16H02 “Inducción automática de taxonomías del español y el francés mediante técnicas cuantitativas y estadística de corpus” (Automatic taxonomy induction from corpora for Spanish and French using quantitative corpus analysis). Lead researcher: Irene Renau. (2016-2019).



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