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courses:rg:2013:composite-activities [2013/04/23 17:23] machys |
courses:rg:2013:composite-activities [2013/09/29 21:35] (current) machys |
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- Thinking about the scripts: | - Thinking about the scripts: | ||
* What is the main reason (the biggest advantage) of using scripts? What kind of information does it bring? (Hint: page 2, page 8) | * What is the main reason (the biggest advantage) of using scripts? What kind of information does it bring? (Hint: page 2, page 8) | ||
- | * The authors don't get the " | + | * The authors don't get the " |
- In the last paragraph of Section 3, a method is described that enhances the robustness of the model (binarization of all association weights < | - In the last paragraph of Section 3, a method is described that enhances the robustness of the model (binarization of all association weights < | ||
* Why does it work? (=> Why should it work best?) | * Why does it work? (=> Why should it work best?) | ||
* Do you have any idea how to do it differently? | * Do you have any idea how to do it differently? | ||
- | - Which tools enhanced the //Attribute recognition// | + | |
+ | |||
+ | ====== Answers ====== | ||
+ | |||
+ | - First set | ||
+ | * list components [[https:// | ||
+ | * dependance of components (the same graph) | ||
+ | - Scripts | ||
+ | * reason?: Cheap source of training data, Many combinations, | ||
+ | * four ways: 2x2: 1) direct use of words from data or 2) mapping word classes from WordNet X 3) simple word frequency or 4) TF*IDF | ||
+ | - There was a discussion about 3rd set of question. We are not sure why authors do that. There was strongly supported opinion that autohors do a lot unnecessary work, which is lost by binarization. | ||
+ | - 4th: Majority people in aswers nominated the use of TF*IDF in case of no training data as the best idea. | ||