Material of the 4th Intensive Summer school and collaborative workshop on Natural Language Processing (NAIST Franco-Thai Workshop 2010). Bangkok, Thaıland. Institution: Institut de Recherche en Informatique de Toulouse (IRIT), Lingua et Machina

1. Franco-Thai Workshop 2010
Lingua et Machina
Research & Development
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2. About me
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Estelle Delpech
Research engineer at Lingua et Machina,
France
CAT tools provider
ed(at)lingua-et-machina(dot)com
www.lingua-et-machina.com
Ph. Candidate at LINA, France
taln team : specialises in NLP
estelle.delpech(at)univ-nantes(dot)fr
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3. LINGUA ET MACHINA
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French company
Founded by Dr E. Planas
Led by Dr. F. De Colstoun
Small but innovative
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8 persons
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2 R&D engineers / Ph. D. candidates
● NLP
● Computational Linguistics
● Translation Studies
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4. LINGUA ET MACHINA
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2002
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SIMILIS
2nd generation translation
memories
Based on Ph.D. work
2007
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LIBELLEX
Access to TM for non-professionals
Translation and terminology
management platform
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5. They trust us
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6. Partners
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7. SIMILIS
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Computer-aided translation
● Free -lance translators
● Translation agencies
Translation memories
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Pre translations
Terminology extraction
7 languages : FR,EN,IT,ES,PT,DE,NL
→ rule based
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8. Similis
Part
1/1
TITLE 1
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9. SIMILIS technology
Based on the Ph. D. work of E. Planas
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First generation translation memory
● Works with segments, sentences
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Second generation translation memory
● Works with chunks
● [the driver] [steps] [on the gas pedal]
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Chunking
● Rules written by linguists
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Fuzzy matching
● Modified edit-distance
● Several linguistic levels
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10. From SIMILIS to LIBELLEX
Source Text
French Documents
Moderator
Memory
(TMX)
Glossary
English Documents
Translated Text
(lexicon)
Moderator
Translators linguists
Business Experts
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11. LIBELLEX
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Translation memories meet corporate content
management
Target : global companies
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Many languages
● customers
● Parterns
● employees
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Speakers
● Non native
● Not language professionals
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Terminology and translations needs
● Official documentation
● Day to day intern communication
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12. Libellex
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Terminology management platform
● builds corporate TM
● extract / check terminology
● help employees communicate
Translation management platform
● manage translations jobs
● terminologies for translation agencies
● chunk matches for MT
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13. Libellex
Part
1/1
TITLE 1
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Look up a word, a term, an expression
Manage terminology
Have a document translated
Check translations
Check text
Add new documents
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14. R-D-I at Lingua et Machina
On going
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Statistical term extraction
● « Cheap and quick » addition of new
languages
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Consider hybridation with rule-based methods
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Term alignment in comparable corpora
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Modelize translation process
Planned
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Development of rule-based chunking on
Chinese
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Extraction of « Knowledge-rich contexts » for
terminologies
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15. Research partnerships
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Statistical term extraction and alignment
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A. Lardilleux, Y. Lepage (Caen/Waseda)
Chinsese processing
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EDF, Kinep
Comparable corpora
● National project + Ph. D. candidate
KRC extraction
● European project submission
Translation studies
● Ph. D. candidate : Stendhal University
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16. Statistical term extraction and
alignment
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Algorithm developed by A. Lardilleux in Ph. D.
Thesis
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http://users.info.unicaen.fr/~alardill/
Uses “perfect alignments“
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Source and target words that only occur in
the same source and target sentences
adf ↔ AD
b ↔ BE
b ↔ CF
a e ↔ AE
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D
R n o ly b ild sm sa p s o co u
adm u s a
ll m
le f rp s
● Perfect alignments add-up
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17. Chinese and other languages
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Chinese processing
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EDF uses Libellex
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Needs ZH↔FR ZH ↔ EN translation
Currently :
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Statistical term alignment and extraction
Planned :
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Chinese chunking rule
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Develop hybrid statistical/rule-based
chunk alignment
Other languages :
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Asian
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Northern european
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Eastern european
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18. Metricc projetc
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Scope : national
Bilingual terminologies mining from
comparable corpora
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CAT
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Translation memories
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CLIR
Partners
● Syllabs, Sinéqua, LM
● IMAG, Valoria
http://www.metricc.com
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19. Metricc : term alignment in comparable
corpora
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Based on distributional analysis hypothesis
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Words that appear in similar contexts
have similar meaning
Represent context of a word in vector :
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Word cooccurrents + normalized
frequencies
Translate context vector with seed lexicon
Compute distance between source and target
vectors
The closer , the better
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20. Knowledge-Rich Contexts Extraction
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Project under submission
Scope : european
Partners :
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Inbenta , BEO
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Lljublana University, LINA
Knowlege-rich contexts
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Help understand the term
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Indicates of to use the term
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21. Knowledge-Rich Contexts Extraction
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Examples of KRC :
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Contains of definition
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Describes a relation between two terms
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Indicates a collocation
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Illustrates the term
KRC linguistic description
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Exemples, definitions in dictionaries
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Corpus study
KRC automatic identification
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Morpho syntactic patterns
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Statistical clues
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22. Modelization of translation process
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Research engineer / Ph. D. Thesis
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Department of translations studies
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Université Stendhal, Grenoble
How do we translate ?
What knowledge is helpful to
translators ?
What is a good translation ?
Do non-professional translate
differently ?
How do you improve software usability
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23. More information
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Lingua et Machina
● www.lingua-et-machina.com/
● contact(a)lingua-et-machina.com
Libellex
● http://libellex.fr/
Download Similis
● http://similis.org/Download/SimilisFreel
ance-2.16.04-Setup.exe
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24. Franco-Thai Workshop 2010
Thank you
ed(a)lingua-et-machina.com
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