A statistical approach to machine translation
Computational Linguistics
Building a large-scale knowledge base for machine translation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
EuroWordNet: a multilingual database with lexical semantic networks
EuroWordNet: a multilingual database with lexical semantic networks
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Char_align: a program for aligning parallel texts at the character level
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Identifying word translations in non-parallel texts
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Selecting target word using contexonym comparison method
Proceedings of the 2007 conference on Human interface: Part I
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The growing importance of multilingual information retrieval and machine translation has made multilingual ontologies extremely valuable resources. Since the construction of an ontology from scratch is a very expensive and time-consuming undertaking, it is attractive to consider ways of automatically aligning monolingual ontologies, which already exist for many of the world's major languages. Previous research exploited similarity in the structure of the ontologies to align, or manually created bilingual resources. These approaches cannot be used to align ontologies with vastly different structures and can only be applied to much studied language pairs for which expensive resources are already available. In this paper, we propose a novel approach to align the ontologies at the node level: Given a concept represented by a particular word sense in one ontology, our task is to find the best corresponding word sense in the second language ontology. To this end, we present a language-independent, corpus-based method that borrows from techniques used in information retrieval and machine translation. We show its efficiency by applying it to two very different ontologies in very different languages: the Mandarin Chinese HowNet and the American English WordNet. Moreover, we propose a methodology to measure bilingual corpora comparability and show that our method is robust enough to use noisy nonparallel bilingual corpora efficiently, when clean parallel corpora are not available.