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KIWI A Multi-Lingual Usage Consultation Tool based on Internet Searching

KIWI A Multi-Lingual Usage Consultation Tool based on Internet Searching. Kumiko TANAKA-Ishii Masato YAMAMOTO Hiroshi NAKAGAWA Language Informatics Laboratory, University of Tokyo. How do you say 「無線 LAN 」 in French?. =. wireless. Never be found in dictionaries…. What could be done.

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KIWI A Multi-Lingual Usage Consultation Tool based on Internet Searching

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  1. KIWIA Multi-Lingual Usage Consultation Tool based on Internet Searching Kumiko TANAKA-Ishii Masato YAMAMOTO Hiroshi NAKAGAWA Language Informatics Laboratory, University of Tokyo

  2. How do you say 「無線LAN」 in French? = wireless Never be found in dictionaries…

  3. What could be done • Look up part of key in the dictionary 　　　無線 → sans fil 2. Enter the translation into search engine

  4. What could be done • Look up part of key in the dictionary 　　　無線 = sans fil 2. Enter the translation into search engine Top 20 le reseau sans fil le net sans fil l’acces sans fil

  5. What could be done • Look up part of key in the dictionary 　　　無線 = sans fil 2. Enter the translation into search engine Sum up top 500 les reseaux sans fil l’internet sans fil

  6. Similar Ocassions…. • Up to date expression * sans fil les reseaux sans fil • Commonness of expression (le reseaux)/(les reseaux) sans fil • Simple Q&A 　　 * Zidane • Grammar check -noun gender un/une langage, un/une langue -preposition discuter ? -articles du/de Japon, du/de Nancy Have clues but can’t remember exactly…

  7. Kiwi Our Idea • Multiple candidates Which one? • Minority candidate 300th candidate? Impossible to manually scan 500 candidates! A tool for scanning search engine results

  8. Related Work 1：www.webcorp.org.ukーThe Web as Corpusー 1999

  9. Related Work 1：www.webcorp.org.ukーThe Web as Corpusー • English only • Sum up fixed length words • Slow!!

  10. Related work 2：Google Fight, Google Duel • Compare the frequency of • 2 phrases • Multilingual

  11. Related work 2：Google Fight, Google Duel • Comparison of two phrases only

  12. Kiwi’s characteristics • Flexible query • - comparison A/B • - wild card 　 *A A*B B* • Multilingual aspect • -String based processing • Language dependent analysis User has clues English web user 36.5% globstats 2002

  13. Online Language Populations http://www.glreach.com/globstats

  14. The Process • Obtain search results summaries only 2. Extract candidates 　*A, A* 3. Order candidates

  15. Characteristics of candidates(at entry being A *) • Frequent • Moderately long • Various succeeding characters

  16. Characteristics of candidates(at entry being A *) } • Frequent • Moderately long • Various succeeding characters } Ordering Extraction

  17. Extraction: number of succeeding character kind cut Branching degree decreases human * increase is _ n a t u r e cut a longer context _ l increase decrease

  18. Ordering -Shorter more frequent Ex. “international” includes “in” Eval-fun (candidate) = freq（candidate)× log (length(candidate) + 1) Empirically defined

  19. Examples german_demo_viewlet_swf.html

  20. German “Atemwegssyndrom” Other candidates ・Respiratorische Syndrom ・oder Chronische Gesundheits ・Erkrankung Etc…

  21. Japanese “重症急性呼吸器” （SARS) Other candidates ・シックハウス (Sick Building) ・エコノミークラス (Economy class) ・慢性疲労 (Chronic Fatigue) Etc…

  22. French “Respiratoire Aigu Sévère” Other candidates ・de Marfan ・Prémenstruel ・de la class économique Etc…

  23. Chinese “嚴重急性呼吸道” Other candidates ・經前 ・電腦視覺 ・腕道 ・睡眠呼吸中止 ・後天免疫缺乏 Etc…

  24. Korean “중증급성호흡기” Other candidates ・급성호흡기 ・만성피로 ・ 과민성 대장 Etc…

  25. Evaluation: English collocations and so on in spite of tail head and so * * spite of 300 300 Kiwi ：1000 match totalized: examine top n (exact match) Baseline ：Search engine results: top n (included or not)

  26. Results ③upper bound of Kiwi n = 10 n = 1000 n = 1 head tail head tail

  27. Results } ③ー②　Extraction error ②ー① Ordering error ＋ test set problem ≠search engine Ex.be anxious for to n = 10 n = 1000 n = 1 head tail head tail

  28. Data amount Rank transition of best & correct candidate Ranking Insufficient Sufficient No. matches

  29. Evaluation Using different search engines n = 10 n = 1 n=1000 Red score is the best score

  30. Related work 3：NL based on search engineEx.Q&A Brill et al.(2002) Obtain Q&A answers from search engine results already totalized results What does this mean to NL?

  31. Comparison of Results

  32. Conclusion • Usage consultation tool up-to-date expression, grammar check • Totalize search engine results • Multi-lingual & flexible entry • String based candidate extraction and ordering • Evaluation

  33. Thank you! Demonstration at ACL (demo session & Univ.Tokyo booth) Please come!

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