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Titlebook: Machine Translation: From Research to Real Users; 5th Conference of th Stephen D. Richardson Conference proceedings 2002 Springer-Verlag Be

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楼主: tricuspid-valve
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Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy ion divergence problem is usually reserved for Transfer and Interlingual MT because it requires a large combination of complex lexical and structural mappings. A major requirement of these approaches is the accessibility of large amounts of explicit symmetric knowledge for both source and target lan
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Merging Example-Based and Statistical Machine Translation: An Experimentbeing able to say that machine translation fully meets the needs of real-life users. In a previous study [.], we have shown how a SMT engine could benefit from terminological resources, especially when translating texts very different from those used to train the system. In the present paper, we dis
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Better Contextual Translation Using Machine Learningf between contextual specificity and general applicability of the mappings, which typically results in conflicting mappings without distinguishing context. We present a machine-learning approach to choosing between such mappings, using classifiers that, in effect, selectively expand the context for
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Fast and Accurate Sentence Alignment of Bilingual Corporaither on sentence length or word correspondences. Sentence-length-based methods are relatively fast and fairly accurate. Word-correspondence-based methods are generally more accurate but much slower, and usually depend on cognates or a bilingual lexicon. Our method adapts and combines these approach
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Deriving Semantic Knowledge from Descriptive Texts Using an MT SystemThe KANT system [.,.] was used to analyze input paragraphs, producing sentence-level interlingua representations. The interlinguas were merged to construct a paragraph-level representation, which was used to create a semantic network in Conceptual Graph (CG) [.] format. The interlinguas are also tra
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