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--- courses:rg:2014:mdsm [2014/11/24 17:16]
nguyenti
+++ courses:rg:2014:mdsm [2014/11/29 13:52] (current)
popel
@@ Line 1: / Line 1: @@
-. Recall from Tam's paper presentation 3 week ago.
+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]].
-What is the difference between:
+The second paper [[http://www.aclweb.org/anthology/P12-1015.pdf|Distributional Semantics in Technicolor]], an extent of the first one, is optional reading.
-distributional semantic and distributed semantic representation
-. What is the vector representation of word "Dog"
- --- //[[dtnguyen88@outlook.com|Nguyen Tien Dat]] 2014/11/24 16:41//
+Q1.
+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
- --- //[[dtnguyen88@outlook.com|Nguyen Tien Dat]] 2014/11/24 16:41//3. What is the similarity and difference between Bag of Word and Bag of Visual Word
+(M_{w,d} is a matrix with w rows and d columns).
+What does w, d and k mean?
+What are the values of w, d and k used in the experiments in this paper?
+Q2.
+a) Compute the similarity between two words "Moon" and "Mars" from the co-occurrence matrix below.
+Use these raw counts (no Local Mutual Information, no normalization) and cosine similarity.
+           | planet | night | full | shadow | shine
+    Moon   |   34   |   27  |  19  |   9    |   20
+    Sun    |   32   |   23  |  10  |   47   |   15
+    Dog    |   0    |   19  |  2   |   11   |   1
+    Mars   |   44   |   23  |  17  |   3    |   9
+b) How do they deal with high dimension of vectors in those papers?
+Can you suggest some (other) techniques of preprocessing vectors with high dimensions?
+Q3.
+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?
+Q4 (bonus).
+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)
+So what is that score? Why did they use it?
+Q5 (bonus).
+Have you ever wished to see beautiful "Mermaids"?
+Have you ever seen "Unicorns" in the real life?
+"Assume that there are no photos of them on the Internet"
+Think about a computational way to show that how they look like?
-.
-. Have you seen "wampimuk" in the real life? Can you imagine how does this animal look like?
-how to you think a way to show that how does it look like?

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