Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
نویسندگان
چکیده
Knowledge-based machine translation ( K B M T ) techniques yield high quabty in domuoH wi th detailed semantic models, l imited vocabulary, and controlled input grammar Scaling up along these dimensions means acquiring large knowledge resources It also means behaving reasonably when definitive knowledge is not yet available This pa per describes how we can fill various K B M T know] edge gap*, often using robust statistical techniques We describe quantitative and qualitative results f rom JAPANGLOSS, a broad-coverage Japanese-
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تاریخ انتشار 1995