Automatic labeling of semantic roles
Computational Linguistics
Estimating Word Translation Probabilities from Unrelated Monolingual Corpora Using the EM Algorithm
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A model for matching semantic maps between languages (French/English, English/French)
Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Mixed language query disambiguation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Base Noun Phrase translation using web data and the EM algorithm
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chinese verb sense discrimination using an EM clustering model with rich linguistic features
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Inducing frame semantic verb classes from WordNet and LDOCE
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
BiFrameNet: bilingual frame semantics resource construction by cross-lingual induction
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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We propose a robust method of automatically constructing a bilingual word sense dictionary from readily available monolingual ontologies by using estimation-maximization, without any annotated training data or manual tuning. We demonstrate our method on the English FrameNet and Chinese HowNet structures. Owing to the robustness of EM iterations in improving translation likelihoods, our word sense translation accuracies are very high, at 82% on average, for the 11 most ambiguous words in the English FrameNet with 5 senses or more. We also carried out a pilot study on using this automatically generated bilingual word sense dictionary to choose the best translation candidates and show the first significant evidence that frame semantics are useful for translation disambiguation. Translation disambiguation accuracy using frame semantics is 75%, compared to 15% by using dictionary glossing only. These results demonstrate the great potential for future application of bilingual frame semantics to machine translation tasks.