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data:start [2008/03/30 13:05]
schtepf 		data:start [2008/06/18 15:46]
schtepf
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   * [[Correlation with Free Association Norms]]   * [[Correlation with Free Association Norms]]
  
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-**NB: On March 29th, we fixed a small (but serious) bug in script ''eval_task3.perl''. If you obtained a copy at an earlier time, please download the most recent version of the package and use it for your evaluation.** 
  
 ==== Task 2: Categorization ==== ==== Task 2: Categorization ====
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   * [[Comparison with Speaker-Generated Features]]   * [[Comparison with Speaker-Generated Features]]
  
-**NB: ON MARCH 7, WE MADE A SMALL CORRECTION TO THE PROPERTY EXPANSION FILE USED FOR THIS TASK; IF YOU DOWNLOADED THE RELEVANT ARCHIVE BEFORE THIS DATE, PLEASE DOWNLOAD IT AGAIN** 
  
 ===== Source corpus ===== ===== Source corpus =====
  
 You can train your word space on your favorite corpus. However, we also invite you, if this is suitable, to experiment with the [[http://wacky.sslmit.unibo.it|ukWaC]] corpus, so that we will be able to compare different word spaces trained on the same corpus (for information on how to obtain the corpus, write to [[wacky@sslmit.unibo.it|this address]]). ukWaC is a very large (about 2 billion tokens) Web-derived corpus. It is split into sub-sections containing randomly chosen documents. Thus, if your algorithm has problems scaling up to 2 billion tokens, you can train it on one or more sub-sections, that will constitute a document-based random sub-sample of ukWaC. You can train your word space on your favorite corpus. However, we also invite you, if this is suitable, to experiment with the [[http://wacky.sslmit.unibo.it|ukWaC]] corpus, so that we will be able to compare different word spaces trained on the same corpus (for information on how to obtain the corpus, write to [[wacky@sslmit.unibo.it|this address]]). ukWaC is a very large (about 2 billion tokens) Web-derived corpus. It is split into sub-sections containing randomly chosen documents. Thus, if your algorithm has problems scaling up to 2 billion tokens, you can train it on one or more sub-sections, that will constitute a document-based random sub-sample of ukWaC.

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