Automatic extraction and manual validation of a hierarchical English-Swedish terminology

Automatic extraction and manual validation of a hierarchical English-Swedish terminology

NORDTERM 2009!

Magnus Merkel*, Jody Foo*, Mikael Andersson**, Lars Edholm**, Mikaela Gidlund**, Sanna Åsberg**!

Presented by Jody Foo!

* Department of Computer and Information Science, Linköping University!

** Fodina Language Technology AB!

Overview!

!! Background!

!! Term extraction and validation process!

!! Results!

!! Conclusions and future work!

Merkel, Foo et al, NORDTERM 2009!

Some history!


2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

First attempts at MT @ EPO2004

Patent Abstracts of Japan (PAJ) launches online machine translation initiative2000

PRV term extraction and validation2008 – 2009

NLPLAB, Linköping University Spin-o!: Fodina Language Technology2004

Patent Information Conference2006

Results from initial machine translation projects

EPO launches patent MT service2006

Machine translation!

!! Two main approaches!!! Rule based machine translation (RBMT), e.g. Babelfish!!! Statistical machine translation (SMT), e.g. Google Translate!

!! MT @ EPO!!! Rule-based MT engine: Systran!!! RBMT requires domain specific dictionaries – patent terms!


Diallo 2006!


Input data!

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A B C D E F G H


Overview of the term extraction and validation process!

OLIF!

Source data analysis and system configuration!

Term candidate extraction!

Term candidate filtering and initial linguistic validation!

Manual validation by domain experts!

Final linguistic validation!

Publishing of validated terms!

SGML & OCR!


Perform necessary steps before term extraction is possible!

OLIF!







SGML & OCR!


Analysis of source material and system configuration!

!"#$%&'(%)*+&,-..+/,0123-44+5/+/,-+/-4/036+.715/073+

!"#$%&'(%&)+&25.-/4+/8712.-2+90:+;<794/=..-/+

*+ *+ >?)>+ @)A+ B*C+ *+

*+ *+ *+>?)>+


Extract list of term candidates to be validated!

OLIF!







SGML & OCR!


Client-server infrastructure!


Reduce the number of term candidates to be processed by the domain experts!

OLIF!







SGML & OCR!


Term filtering and initial linguistic validation!

!! Filtering criteria!!! General language filtering!!! Q-value (~alignment confidence)!!! Link errors!!! Source OR target frequency > 4!


Term filtering and initial linguistic validation!

!! Example: C04B!

Total number of term candidates: 143,341 General language entries: 18,764 Link errors: 653 Freq >4 src|trg: 9,064 Q-value filtering: keep 4,076 DEF95.G(HIJ+

Total after filtering: 3,179!


Overview of the term extraction and validation process!

OLIF!







SGML & OCR!



!! To be validated!!! Part-of-speech, Inflection pattern, Gender, Number!

!! Recycle as much information as possible from previously validated terms!

!! Process terms by recycling status!!! Very reliable information!!! Less reliable information!!! No information available!



C21B! C21C! C21D!

C21!C03!

C!

H05C!H05B!

A! F! H!

C03B! C03C!

F42! H05!C11!A61!

Top!

E!


Final numbers!

!! Processed 91,000 document pairs in 8 months.!

!! Validated term pairs: 181,260!

!! Expert validatation: 4 – 6,000 term candidate pairs/working day!

!! Linguistic validation: 2 – 3,000 term pairs/working day!


Section!Accumulated amount

of total number of documents (in %)!

Accumulated amount of total number of

documents (in %)!

Accumulated amount of term pairs!

Accumulated amount of UNIQUE term pairs!

D! 2,8! 2,8! 17288! 9697!E! 2,1! 4,9! 32045! 16304!F! 7,1! 12! 78301! 32512!G! 10,2! 22,2! 133912! 53731!H! 10,3! 32,5! 187429! 72721!A! 20,7! 53,2! 289850! 110642!B! 18,1! 71,3! 419185! 146665!C! 28,7! 100! 545143! 181260!

Growth of validated terms!


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Amount of total number of documents (in %)!A blue diamond marks the right edge of a section, left to right: D - E - F - G - H - A - B - C.!

Accumulated amount of validated term pairs!

Accumulated amount of validated UNIQUE term pairs!

Right section edge of: D - E - F - G - H - A - B - C!

Conclusions and future work!

!! Key concepts!!! using previously validated term pairs to avoid doing the same

work twice!!! using students as domain experts!!! using an e"cient validation tool!

!! Future work!!! Improving automated filtering and reduction of term candidates!!! Automating termness detection!


Automatic extraction and manual validation of a hierarchical English-Swedish terminology

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