A parameterized approach to integrating aspect with lexical-semantics for machine translation

This paper discusses how a two-level knowledge representation model for machine translation integrates aspectual information with lexical-semantic information by means of parameterization. The integration of aspect with lexical-semantics is especially critical in machine translation because of the lexical selection and aspectual realization processes that operate during the production of the target-language sentence: there are often a large number of lexical and aspectual possibilities to choose from in the production of a sentence from a lexical semantic representation. Aspectual information from the source-language sentence constrains the choice of target-language terms. In turn, the target-language terms limit the possibilities for generation of aspect. Thus, there is a two-way communication channel between the two processes. This paper will show that the selection/realization processes may be parameterized so that they operate uniformly across more than one language and it will describe how the parameter-based approach is currently being used as the basis for extraction of aspectual information from corpora.