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courses:rg:2014:mdsm [2014/11/29 01:55]
nguyenti 		courses:rg:2014:mdsm [2014/11/29 13:52] (current)
popel
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-You should focus on the first paper (skip section 2.3) +You should focus on the first paper (you can skip section 2.3): [[http://www.aclweb.org/anthology/W11-2503.pdf|Distributional semantics from text and images]]. 
-The second paper, an extent of the first one, is optional reading.+The second paper [[http://www.aclweb.org/anthology/P12-1015.pdf|Distributional Semantics in Technicolor]], an extent of the first one, is optional reading.
  
  
 Q1.  Q1. 
-a)Recall the paper about word representations presented by Tam on November 10.+Recall the paper about word representations presented by Tam on November 10.
 Read http://www.quora.com/Whats-the-difference-between-distributed-and-distributional-semantic-representations Read http://www.quora.com/Whats-the-difference-between-distributed-and-distributional-semantic-representations
  
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 What does w, d and k mean? What does w, d and k mean?
 What are the values of w, d and k used in the experiments in this paper? What are the values of w, d and k used in the experiments in this paper?
- 
-b) What is maximum dimension of a word vector in distributional representation approach? 
  
 Q2. Q2.
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 b) How do they deal with high dimension of vectors in those papers? b) How do they deal with high dimension of vectors in those papers?
-Can you suggest some other techniques to manage vector dimension?+Can you suggest some (other) techniques of preprocessing vectors with high dimensions?
            
 Q3.  Q3. 
 a) What are Bag of Word (BOVW) and Bag of Visual Word (BOW)? a) What are Bag of Word (BOVW) and Bag of Visual Word (BOW)?
-b) How do they apply BOVW to compute representation of a word (concept) from a large set of Images?+b) How do they apply BOVW to compute representation of a word (concept) from a large set of images?
        
-Q4.+Q4 (bonus).
 When they construct text-based vectors of words from DM model When they construct text-based vectors of words from DM model
 they mentioned Local Mutual Information score. (section 3.2, also section 2.1 in the 2nd paper) they mentioned Local Mutual Information score. (section 3.2, also section 2.1 in the 2nd paper)
 So what is that score? Why did they use it? So what is that score? Why did they use it?
  
-Q5:+Q5 (bonus).
 Have you ever wished to see beautiful "Mermaids"? Have you ever wished to see beautiful "Mermaids"?
 Have you ever seen "Unicorns" in the real life? Have you ever seen "Unicorns" in the real life?
-Assume that there are no photos of them on the Internet.+"Assume that there are no photos of them on the Internet"
  
 Think about a computational way to show that how they look like? Think about a computational way to show that how they look like?
  
  

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