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Linguistic Hacking How to know what a text in an unknown language is about? Martin.Haase@uni-bamberg.de maha@jabber.ccc.de 1 Contents how to identify the language of a written text in traditional ways, with the help of computer


  1. Linguistic Hacking How to know what a text in an unknown language is about? Martin.Haase@uni-bamberg.de maha@jabber.ccc.de 1

  2. Contents • how to identify the language of a written text • in traditional ways, • with the help of computer technology. • how to get at least some information out of an unknown text. 2

  3. Hacking “the intellectual challenge of creatively overcoming or circumventing limitations.” Eric Raymond (1996): The New Hacker ʼ s Dictionary . 3

  4. Spoken texts? multi-language corpus of telephone calls 4

  5. Writing Systems • Roman (thousands of languages) • Cyrillic (> 60 languages) • Arabic (> 20 languages) • Devana ̅ gar ī (> 10 languages, not counting derivative writing systems) • Hebrew (~ 3 – 5 languages) 5

  6. Devan ā gar ī �वनागरी 6

  7. 7

  8. 8

  9. 9

  10. 10

  11. Hebrew • Old and Modern Hebrew, • Ladino (with di fg erent varieties), • Judeo-Arabic, • Yiddish. 11

  12. 12

  13. 13

  14. Norman C. Ingle (1980): Language Identification Table. London: Technical Translation International. 14

  15. 15

  16. Computer-aided identification • frequencies of unique characters and character strings • common words recognition • n-gram analysis 16

  17. “Text” 17

  18. ( TE), (TEX), (EXT), (XT ) 18

  19. 19

  20. variant of the unique character string approach 20

  21. compression e ffj ciency 21

  22. reference model 22

  23. reference text in language + model to be identified 23

  24. gzip reference text in language + model to be identified 24

  25. gzip reference text in language + model to be identified compression e ffj ciency 25

  26. Interesting applications • measuring linguistic difference > language families • determining types of text • spam detection? 26

  27. • TextCat (http://odur.let.rug.nl/vannoord/TextCat/Demo/), n-gram based, 76 languages, usable as a web application, • Languid (http://languid.cantbedone.org/), downloadable program, web application not running, • Langid (http://complingone.georgetown.edu/ ∼ langid/), n-gram based, 65 languages, web application, • LanguageGuesser (http://www.xrce.xerox.com/cgi-bin/mltt/ LanguageGuesser), frequency tests on characters and character sequences, about 40 languages, web application, • Polyglot 3000 (http://www.polyglot3000.com/), corpora, method unknown, currently 441 languages, closed-source Windows freeware. :-( 27

  28. approaching “content analysis” 28

  29. Hacker ʼ s approach • numbers, dates, words from another language • typographic hints: • bold or italic print, • colored or underlined text chunks, • capital letters 29

  30. Zipf ʼ s law Very frequent words are shorter and contain less lexical information, whereas infrequent words are longer and contain more lexical information. 30

  31. less lexical information implies more grammatical information and vice versa 31

  32. most interesting for us: words with more specific lexical information 32

  33. Ignore all short words! (even if they reiterate throughout the text) 33

  34. Ua salalau lenei gagana i le lalolagi atoa. ‘O lenei fo‘i gagana, ‘ua ‘avea ma gagana lona lua a le tele o tagata ‘o le vasa Pasefika, e pei ‘o Samoa. E iai le manatu, ‘o le gagana fa‘aperetania, ‘ua matua ̅ talitonu i ai le tele o tagata Samoa e fa‘apea ‘o le gagana e maua ai le atamai ma le poto. ‘E talitonu fo‘i nisi o i latou, ‘e le ̅ aoga la latou gagana. E le ̅ sa‘o lea ta ̅ ofi, ‘aua ̅ e ‘avatu le gagana fa‘aperetania i Samoa, ‘ua leva ona atamamai ma popoto tagata Samoa e fai lo latou soifua ma lo latou lalolagi. 34

  35. Ua salalau lenei gagana i le lalolagi atoa. ‘O lenei fo‘i gagana, ‘ua ‘avea ma gagana lona lua a le tele o tagata ‘o le vasa Pasefika, e pei ‘o Samoa. E iai le manatu, ‘o le gagana fa‘aperetania, ‘ua matua ̅ talitonu i ai le tele o tagata Samoa e fa‘apea ‘o le gagana e maua ai le atamai ma le poto. ‘E talitonu fo‘i nisi o i latou, ‘e le ̅ aoga la latou gagana. E le ̅ sa‘o lea ta ̅ ofi, ‘aua ̅ e ‘avatu le gagana fa‘aperetania i Samoa, ‘ua leva ona atamamai ma popoto tagata Samoa e fai lo latou soifua ma lo latou lalolagi. 35

  36. Ua salalau lenei gagana i le lalolagi atoa. ‘O lenei fo‘i gagana, ‘ua ‘avea ma gagana lona lua a le tele o tagata ‘o le vasa Pasefika, e pei ‘o Samoa. E iai le manatu, ‘o le gagana fa‘aperetania, ‘ua matua ̅ talitonu i ai le tele o tagata Samoa e fa‘apea ‘o le gagana e maua ai le atamai ma le poto. ‘E talitonu fo‘i nisi o i latou, ‘e le ̅ aoga la latou gagana. E le ̅ sa‘o lea ta ̅ ofi, ‘aua ̅ e ‘avatu le gagana fa‘aperetania i Samoa, ‘ua leva ona atamamai ma popoto tagata Samoa e fai lo latou soifua ma lo latou lalolagi. 36

  37. Ua salalau lenei gagana i le lalolagi atoa. ‘O lenei fo‘i gagana, ‘ua ‘avea ma gagana lona lua a le tele o tagata ‘o le vasa Pasefika, e pei ‘o Samoa. E iai le manatu, ‘o le gagana fa‘aperetania, ‘ua matua ̅ talitonu i ai le tele o tagata Samoa e fa‘apea ‘o le gagana e maua ai le atamai ma le poto. ‘E talitonu fo‘i nisi o i latou, ‘e le ̅ aoga la latou gagana. E le ̅ sa‘o lea ta ̅ ofi, ‘aua ̅ e ‘avatu le gagana fa‘aperetania i Samoa, ‘ua leva ona atamamai ma popoto tagata Samoa e fai lo latou soifua ma lo latou lalolagi. 37

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