Machine Translation: A View from the Lexicon (Artificial Intelligence)

This book describes a novel, cross-linguistic approach to machine translation that solves certain classes of syntactic and lexical divergences by means of a lexical conceptual structure that can be composed and decomposed in language-specific ways. This approach allows the translator to operate uniformly across many languages, while still accounting for knowledge that is specific to each language.The translation model can be used to map a source-language sentence to a target-language sentence in a principled fashion. It is built on the basis of a parametric approach, making it easy to change from one language to another (by setting syntactic switches for each language and providing lexical descriptions for each language) without having to write a whole new processor for each language.Dorr's approach advances the field of machine translation in a number of important ways: it provides a uniform processor in which the same syntactic and lexical-semantic processing modules are used for each language; it is interlingual, able to derive an underlying language-independent form of the source language that allows any of the three target languages - Spanish, English, or German - to be produced from this form; and it describes a systematic mapping between the lexical-semantic level and the syntactic level that allows the appropriate target-language words to be selected and realized, despite the potential for syntactic and lexical divergences.Bonnie Jean Dorr is Assistant Professor in the Computer Science Department at the University of Maryland.