English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

A Machine Learning Approach to Building Aligned Wordnets

MPS-Authors
/persons/resource/persons44292

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

/persons/resource/persons45720

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

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1AD5-E
Abstract
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.