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Abstract. We report research on semantic relations extraction to build taxonomies. The state of the art approaches are based on text corpus or on domain texts acquisition to accurately characterize the domain of interest. We analyzed the application of unsupervised methods for ontology building using a collection of opinion reviews in Spanish and the Web. We present some results and discuss the obtained relations.

Abstract. Malapropisms are real-word errors that lead to syntactically correct but semantically implausible text. We report an experiment on detection and correction of Spanish malapropisms. Malapropos words semantically destroy collocations (syntactically connected word pairs) they are in. Thus we detect possible malapropisms as words that do not form semantically plausible collocations with neighboring words. As correction candidates, we select words similar to the suspected one but forming plausible collocations with neighboring words. To judge semantic plausibility of a collocation, we use Google statistics of occurrences of the word combination and of the two words taken apart. Since collocation components can be separated by other words in a sentence, Google statistics is gathered for the most probable distance between them. The statistics is recalculated to a specially defined Semantic Compatibility Index (SCI). Heuristic rules are proposed to signal malapropisms when SCI values are lower than a predetermined threshold and to retain a few highly SCIranked correction candidates. Our experiments gave promising results.