Automatic extraction and manual validation of a hierarchical English-Swedish terminology
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Several methods can be applied to create a set of validated terms from existing documents. In this paper we describe an automatic bilingual term candidate extraction method, and the validation process used to create a hierarchical patent terminology. The process described was used to extract terms from patent texts, commissioned by the Swedish Patent Office with the purpose of using the terms for machine translation. Information on the correct linguistic inflection patterns and hierarchical partitioning of terms based on their use are of utmost importance. The process contains six phases, 1) Analysis of the source material and system configuration; 2) Term candidate extraction; 3) Term candidate filtering and initial linguistic validation; 4) Manual validation by domain experts; 5) Final linguistic validation; and 6) Publishing the validated terms. Input to the extraction process consisted of more than 91.000 patent document pairs in English and Swedish, 565 million words in English and 450 million words in Swedish. The English documents were supplied in EBD SGML format and the Swedish documents were supplied in OCR processed scans of patent documents. After grammatical and statistical analysis, the documents were word aligned. Using the word aligned material, candidate terms were extracted based on linguistic patterns. 750,000 term candidates were extracted and stored in a relational database. The term candidates were processed in 8 months resulting in 181.000 unique validated term pairs which were then exported into several hierarchically organized OLIF files.
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Automatic extraction and manual validation of a hierarchical English-Swedish terminology
- 1. 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
- 2. Overview !! Background !! Term extraction and validation process !! Results !! Conclusions and future work Merkel, Foo et al, NORDTERM 2009
- 3. Some history NLPLAB, Linköping University Spin-o : Fodina Language Technology 2004 Patent Information Conference 2006 Results from initial machine translation projects Patent Abstracts of Japan (PAJ) launches online machine translation initiative EPO launches patent MT service 2000 2006 First attempts at MT @ EPO PRV term extraction and validation 2004 2008 – 2009 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Merkel, Foo et al, NORDTERM 2009
- 4. 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 Merkel, Foo et al, NORDTERM 2009
- 5. Diallo 2006 Merkel, Foo et al, NORDTERM 2009
- 6. Diallo 2006 Merkel, Foo et al, NORDTERM 2009
- 7. 0 1000 2000 3000 4000 5000 6000 7000 8000 A01B A22C A41B A45D A61D 0 2000 4000 6000 8000 10000 12000 14000 A63D A01 B04B A23 B21G A42 B23P A45 B27B A61 B29C B01 B41N Input data B04 B60F B07 B61C B21 B62M B65G B24 C01D B27 C06F B30 C08K B41 C10K B44 C12S B62 C23G B65 D03J B68 D06P C02 E02B C05 E05D C08 F01N C11 F04B 0 5000 10000 15000 20000 25000 30000 C14 F16L C23 F21S D01 A F23Q Merkel, Foo et al, NORDTERM 2009 F28B D04 G01B D07 B G01V E02 G05B E05 G07D F01 C G11B F04 H01J F17 H02N D F23 H04K F26 F41 E G02 G05 G08 F G11 H01 H04 G H
- 8. Merkel, Foo et al, NORDTERM 2009
- 9. Overview of the term extraction and validation process Source data analysis and system Term candidate Term candidate filtering and SGML & configuration extraction initial linguistic validation OCR Manual validation by domain Final linguistic Publishing of validated experts validation terms OLIF Merkel, Foo et al, NORDTERM 2009
- 10. Perform necessary steps before term extraction is possible Source data analysis and system Term candidate Term candidate filtering and SGML & configuration extraction initial linguistic validation OCR Manual validation by domain Final linguistic Publishing of validated experts validation terms OLIF Merkel, Foo et al, NORDTERM 2009
- 11. Analysis of source material and system configuration *+ *+ >?)>+ @)A+ B*C+ *+ !"#$%&'(%)*+&,-..+/,0123-44+5/+/,-+/-4/036+.715/073+ !"#$%&'(%&)+&25.-/4+/8712.-2+90:+;<794/=..-/+ *+ *+ >?)>+ *+ Merkel, Foo et al, NORDTERM 2009
- 12. Extract list of term candidates to be validated Source data analysis and system Term candidate Term candidate filtering and SGML & configuration extraction initial linguistic validation OCR Manual validation by domain Final linguistic Publishing of validated experts validation terms OLIF Merkel, Foo et al, NORDTERM 2009
- 13. Term candidate extraction Merkel, Foo et al, NORDTERM 2009
- 14. Client-server infrastructure Merkel, Foo et al, NORDTERM 2009
- 15. Merkel, Foo et al, NORDTERM 2009
- 16. Reduce the number of term candidates to be processed by the domain experts Source data analysis and system Term candidate Term candidate filtering and SGML & configuration extraction initial linguistic validation OCR Manual validation by domain Final linguistic Publishing of validated experts validation terms OLIF Merkel, Foo et al, NORDTERM 2009
- 17. Term filtering and initial linguistic validation !! Filtering criteria !! General language filtering !! Q-value (~alignment confidence) !! Link errors !! Source OR target frequency > 4 Merkel, Foo et al, NORDTERM 2009
- 18. 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 Merkel, Foo et al, NORDTERM 2009
- 19. Manual validation by domain experts Merkel, Foo et al, NORDTERM 2009
- 20. Overview of the term extraction and validation process Source data analysis and system Term candidate Term candidate filtering and SGML & configuration extraction initial linguistic validation OCR Manual validation by domain Final linguistic Publishing of validated experts validation terms OLIF Merkel, Foo et al, NORDTERM 2009
- 21. Final linguistic validation !! 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 Merkel, Foo et al, NORDTERM 2009
- 22. Publishing of validated terms Top A C E F H A61 C03 C11 F42 H05 C21 C03B C03C C21B C21C C21D H05B H05C Merkel, Foo et al, NORDTERM 2009
- 23. 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 Accumulated amount Accumulated amount of Accumulated amount Accumulated amount Section of total number of total number of of UNIQUE term of term pairs documents (in %) documents (in %) 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 Merkel, Foo et al, NORDTERM 2009
- 24. Growth of validated terms 600000 Accumulated amount of validated term pairs Number of validated term pairs 500000 Accumulated amount of 400000 validated UNIQUE term pairs 300000 Right section edge of: D -E-F-G-H-A-B-C 200000 100000 0 0 20 40 60 80 100 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. Merkel, Foo et al, NORDTERM 2009
- 25. 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 Merkel, Foo et al, NORDTERM 2009