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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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樓主: tricuspid-valve
31#
發(fā)表于 2025-3-26 23:58:03 | 只看該作者
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
32#
發(fā)表于 2025-3-27 01:57:00 | 只看該作者
33#
發(fā)表于 2025-3-27 05:58:34 | 只看該作者
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
34#
發(fā)表于 2025-3-27 09:44:51 | 只看該作者
35#
發(fā)表于 2025-3-27 14:48:12 | 只看該作者
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
36#
發(fā)表于 2025-3-27 21:16:43 | 只看該作者
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
37#
發(fā)表于 2025-3-28 00:50:24 | 只看該作者
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
38#
發(fā)表于 2025-3-28 04:45:34 | 只看該作者
39#
發(fā)表于 2025-3-28 09:41:59 | 只看該作者
40#
發(fā)表于 2025-3-28 13:18:05 | 只看該作者
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