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A Machine Learning Approach to Building Aligned Wordnets

MPG-Autoren
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de Melo,  Gerard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

de Melo, G., & Weikum, G. (2008). A Machine Learning Approach to Building Aligned Wordnets. In Proceedings of the First International Conference on Global Interoperability for Language Resources (pp. 163-170). Hong Kong: Department of Chinese, Translation and Linguistics, City University of Hong Kong.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-000F-1AD5-E
Zusammenfassung
WordNet is a lexical database describing English words and their senses. We propose a method for automatically producing similar resources for new languages by taking advantage of the original WordNet in conjunction with translation dictionaries. A small set of training mappings is used to learn a model for predicting associations between terms and senses. The associations are represented using a variety of scores that take into account structural properties as well as semantic relatedness and corpus frequency information. For evaluation, we created a German-language wordnet, and the data indicate a significantly better coverage and higher precision than previous heuristics. The resulting resources provide not only valuable information for monolingual NLP tasks but also enable a high degree of cross-lingual interoperability.