Taking on new challenges in multi-word unit processing for machine translation
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Johanna MONTI, Anabela BARREIRO, Annibale ELIA, Federica MARANO, Antonella NAPOLI
Taking on new challenges in multi-word unit processing for machine
translation
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Outline
2Taking on new challenges in Multi-word Unit processing for machine translation – FreeBMT 2011
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Multi-word units in the Lexicon-Grammar: definition
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Multi-word units in the Lexicon-Grammar
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Multi-word units in the Lexicon-Grammar : part of a continuum
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Multi-word units in the Lexicon-Grammar: lemmatization
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Multi-word units in the Lexicon-Grammar: lemmatization criteria
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The corpus-linguistic approach
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Multi-word units in Machine Translation
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Multi-word units in Machine Translation: main problems
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Multi-word units in Machine Translation: different approaches
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Lexical ambiguities handled by different systems
• Corpus: non-specialized texts approx. 300 sentences (10,000 words) multi-word units extracted from the Web
Webcorp LSE, Web as a Corpus
• MT systems : Google TranslateOpenLogos
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Typical ambiguities: examples
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Typical ambiguities: examples
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Integration of Semantico-Syntactic knowledge
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Integration of Semantico-Syntactic knowledge
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Integration of Semantico-Syntactic knowledge: mix up
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Semantic table (SEMTAB ) rule
Italian Transfer
MIX UP(VT) IN MESCOLARE IN
MIX UP(VT) N IN MESCOLARE N IN
MIX UP(VT) N WITH CONFONDERE N CON
MIX UP(VT) N(HUMAN) IN CONFONDERE N IN
MIX UP(VT) N(INGREDIENT) MESCOLARE N
MIX UP(VT) N(MEDICINE) PREPARARE N
MIX UP(VT) WITH CONFONDERE CON
MIX UP(VT) N(HUMAN,INFO) WITH CONFONDERE N CON
SemTab rules comment lines for the verb mix up
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Qualitative MT Evaluation metrics
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Qualitative MT Evaluation metrics
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Qualitative MT Evaluation metrics
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Qualitative MT Evaluation metrics
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Qualitative MT Evaluation metrics: the «ideal» evaluation tool
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Conclusions
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Johanna MONTI, Anabela BARREIROAnnibale ELIA, Federica MARANO, Antonella NAPOLI
Thank you for your attention !