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Task ideas for Google Code-in/Grow bilingual

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# '''select a language pair''', ideally such that the source language is a language you know (L₂) and the target language a language you use every day (L₁), such that it has rather good monolingual dictionaries in Apertium but no reasonable bilingual dictionary (these language pairs are usually in the incubator), for instance apertium-spa-pol
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# '''Select a language pair''', ideally such that the source language is a language you know (L₂) and the target language a language you use every day (L₁), such that it has rather good monolingual dictionaries in Apertium but no reasonable bilingual dictionary (these language pairs are usually in the incubator), for instance apertium-spa-pol
# '''Install Apertium''' locally from the Subversion repository; install the language pair; make sure that it works and/or get [http://wiki.apertium.org/wiki/Apertium_VirtualBox Apertium VirtualBox] and update, check out & compile the language pair.
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# '''Install Apertium''' locally from nightlies; clone the relevant language modules and pair from GitHub; make sure that it works. Alternatively, get [http://wiki.apertium.org/wiki/Apertium_VirtualBox Apertium VirtualBox] and update, check out & compile the language pair.
# Using a large enough corpus of representative text in the source language (e.g. plain text taken from Wikipedia, newspapers, literature, etc.) '''detect the 200 most frequent unknown words''' (source words which are not in the bilingual dictionaries of the language pair).
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# Using a large enough corpus of representative text in the source language (e.g. plain text taken from Wikipedia, newspapers, literature, etc.) '''detect the 200 most frequent unknown words''' (source words which are not in the bilingual dictionaries of the language pair). See
# '''add these correspondences to the bilingual dictionary''' (the appropriate <code>.dix</code> file) in [[bidix]] format (so that they are not unknown anymore).
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# '''Add these correspondences to the bilingual dictionary''' (the appropriate <code>.dix</code> file) in [[bidix]] format (so that they are not unknown anymore), as well as the monolingual analysers if needed. Make sure to categorise stems correctly.
 
# '''Compile and test again'''
 
# '''Compile and test again'''
# '''Submit''' a patch to your mentor (or commit it if you have already gained developer access)
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# '''Submit''' a pull request to the GitHub repositories
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==How to find the most frequent unknowns==
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<pre>
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<Unhammer> translate your corpus, make it one word per line, grab only the
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ones with * at the start, sort, count number of hits per word, sort
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again
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<Unhammer> e.g. [11:25]
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<Unhammer> zcat corpus.txt.gz | apertium -d . ron-fra | tr ' ' '\n' | grep
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'^\*' | sort |uniq -d |sort -n >hitlist
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<asusAndrei> awesome! [11:26]
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<Unhammer> hitlist will be unknowns sorted by frequency, but you might have to
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skip a couple that are "strange" or difficult to add
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<Unhammer> and that's ok, as long as you start from the most frequent and work
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your way down
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</pre>
   
 
[[Category:Tasks for Google Code-in|Grow bilingual]]
 
[[Category:Tasks for Google Code-in|Grow bilingual]]

Revision as of 19:22, 8 November 2019

  1. Select a language pair, ideally such that the source language is a language you know (L₂) and the target language a language you use every day (L₁), such that it has rather good monolingual dictionaries in Apertium but no reasonable bilingual dictionary (these language pairs are usually in the incubator), for instance apertium-spa-pol
  2. Install Apertium locally from nightlies; clone the relevant language modules and pair from GitHub; make sure that it works. Alternatively, get Apertium VirtualBox and update, check out & compile the language pair.
  3. Using a large enough corpus of representative text in the source language (e.g. plain text taken from Wikipedia, newspapers, literature, etc.) detect the 200 most frequent unknown words (source words which are not in the bilingual dictionaries of the language pair). See
  4. Add these correspondences to the bilingual dictionary (the appropriate .dix file) in bidix format (so that they are not unknown anymore), as well as the monolingual analysers if needed. Make sure to categorise stems correctly.
  5. Compile and test again
  6. Submit a pull request to the GitHub repositories

How to find the most frequent unknowns

<Unhammer> translate your corpus, make it one word per line, grab only the
           ones with * at the start, sort, count number of hits per word, sort
           again
<Unhammer> e.g.                                                         [11:25]
<Unhammer> zcat corpus.txt.gz | apertium -d . ron-fra | tr ' ' '\n' | grep
           '^\*' | sort |uniq -d |sort -n >hitlist
<asusAndrei> awesome!                                                   [11:26]
<Unhammer> hitlist will be unknowns sorted by frequency, but you might have to
           skip a couple that are "strange" or difficult to add
<Unhammer> and that's ok, as long as you start from the most frequent and work
           your way down
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