Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
This paper compares four algorithms for computing feature-based similarities between concepts respectively possessing a distinctive set of features. The eventual purpose of comparing these feature-based similarity algorithms is to identify a candidate term in a Target Language (TL) that can optimally convey the original meaning of a culturally-specific Source Language (SL) concept to a TL audience by aligning two culturally-dependent domain-specific ontologies. The results indicate that the Bayesian Model of Generalization  performs best, not only for identifying candidate translation terms, but also for computing probabilities that an information receiver successfully infers the meaning of an SL concept from a given TL translation.
|Title||Proceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science, NLPCS 2012|
|Workshop||9th International Workshop on Natural Language Processing and Cognitive Science (NLPCS 2012)|
|Period||28/06/12 → …|
- Algorithms, Bayesian networks, Computational linguistics, Translation (languages), Natural language processing systems
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