Lonsdale, D.; Mitamura, T.; Nyberg, E.: Acquisition of large lexicons for practical knowledge-based MT (1994/95)
0.01
0.0059357807 = product of:
0.023743123 = sum of:
0.023743123 = product of:
0.047486246 = sum of:
0.047486246 = weight(_text_:software in 7409) [ClassicSimilarity], result of:
0.047486246 = score(doc=7409,freq=2.0), product of:
0.18056466 = queryWeight, product of:
3.9671519 = idf(docFreq=2274, maxDocs=44218)
0.045514934 = queryNorm
0.2629875 = fieldWeight in 7409, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9671519 = idf(docFreq=2274, maxDocs=44218)
0.046875 = fieldNorm(doc=7409)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Although knowledge based MT systems have the potential to achieve high translation accuracy, each successful application system requires a large amount of hand coded lexical knowledge. Systems like KBMT-89 and its descendants have demonstarted how knowledge based translation can produce good results in technical domains with tractable domain semantics. Nevertheless, the magnitude of the development task for large scale applications with 10s of 1000s of of domain concepts precludes a purely hand crafted approach. The current challenge for the next generation of knowledge based MT systems is to utilize online textual resources and corpus analysis software in order to automate the most laborious aspects of the knowledge acquisition process. This partial automation can in turn maximize the productivity of human knowledge engineers and help to make large scale applications of knowledge based MT an viable approach. Discusses the corpus based knowledge acquisition methodology used in KANT, a knowledge based translation system for multilingual document production. This methodology can be generalized beyond the KANT interlinhua approach for use with any system that requires similar kinds of knowledge
