johanna monti, anabela barreiro, annibale elia, federica marano, antonella napoli taking on new...
TRANSCRIPT
Johanna MONTI, Anabela BARREIRO, Annibale ELIA, Federica MARANO, Antonella NAPOLI
Taking on new challenges in multi-word unit processing for machine
translation
Outline
2Taking on new challenges in Multi-word Unit processing for machine translation – FreeBMT 2011
Multi-word units in the Lexicon-Grammar: definition
3Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Multi-word units in the Lexicon-Grammar
4Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Multi-word units in the Lexicon-Grammar : part of a continuum
5Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Multi-word units in the Lexicon-Grammar: lemmatization
6Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Multi-word units in the Lexicon-Grammar: lemmatization criteria
7Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
The corpus-linguistic approach
8Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Multi-word units in Machine Translation
9Taking on new challenges in Multi-word Unit processing for machine translation –
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Multi-word units in Machine Translation: main problems
10Taking on new challenges in Multi-word Unit processing for machine translation –
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Multi-word units in Machine Translation: different approaches
Taking on new challenges in Multi-word Unit processing for machine translation – FreeBMT 2011
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
12Taking on new challenges in Multi-word Unit processing for machine translation –
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Typical ambiguities: examples
13Taking on new challenges in Multi-word Unit processing for machine translation –
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Typical ambiguities: examples
14Taking on new challenges in Multi-word Unit processing for machine translation –
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Integration of Semantico-Syntactic knowledge
15Taking on new challenges in Multi-word Unit processing for machine translation –
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Integration of Semantico-Syntactic knowledge
16Taking on new challenges in Multi-word Unit processing for machine translation –
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Integration of Semantico-Syntactic knowledge: mix up
17Taking on new challenges in Multi-word Unit processing for machine translation –
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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
Taking on new challenges in Multi-word Unit processing for machine translation – FreeBMT 2011
Qualitative MT Evaluation metrics
19Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Qualitative MT Evaluation metrics
20Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Qualitative MT Evaluation metrics
21Taking on new challenges in Multi-word Unit processing for machine translation –
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Qualitative MT Evaluation metrics
22Taking on new challenges in Multi-word Unit processing for machine translation –
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Qualitative MT Evaluation metrics: the «ideal» evaluation tool
23Taking on new challenges in Multi-word Unit processing for machine translation –
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Conclusions
24Taking on new challenges in Multi-word Unit processing for machine translation –
FreeBMT 2011
Johanna MONTI, Anabela BARREIROAnnibale ELIA, Federica MARANO, Antonella NAPOLI
Thank you for your attention !