Processing dictionary definitions with phrasal pattern hierarchies
Computational Linguistics - Special issue of the lexicon
Detecting patterns in a Lexical Data Base
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Multilingual Evidence Improves Clustering-based Taxonomy Extraction
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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In this paper a method is presented which permits to automatically extract lexical-semantic relations between nouns (specifically for concrete nouns since they have a well structured taxonomy). From the definitions of the entries in a Spanish dictionary, the hypernym of an entry is extracted from the entry definition according to the basic assumption that the first noun in the definition is the entry hypernym. After obtaining the hypernym for each entry, multilayered hyponymy-hyperonymy relations are generated from a noun, which is considered the root of the domain. The domains for which this approach was tested were zoology and botany. Five levels of hyponymy-hypernymy relations were generated for each domain. For the zoology domain a total of 1,326 relations was obtained with an average percentage of correctly generated relations (precision) of 84.31% for the five levels. 91.32% of all the relations of this domain were obtained in the first three levels, and for each of these levels the precision exceeds 96%. For the botany domain a total of 1,199 relations was obtained, with an average precision of 71.31% for the five levels. 90.76% of all the relations of this domain were obtained in the first level, and for this level the precision exceeds 99%.