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--- courses:rg:2014:mdsm [2014/11/25 21:28]
nguyenti
+++ courses:rg:2014:mdsm [2014/11/29 13:52] (current)
popel
@@ Line 1: / Line 1: @@
-Q1. Recall from the paper presented by Tam 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]].
-a. What is the difference between distributional semantic and distributed semantic representation?
+The second paper [[http://www.aclweb.org/anthology/P12-1015.pdf|Distributional Semantics in Technicolor]], an extent of the first one, is optional reading.
-b. What is maximum size of word vector in distributional representation approach?
+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
+(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 "Sun" from the co-occurrence matrix below:
+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
@@ Line 12: / Line 21: @@
     Mars   |   44   |   23  |  17  |   3    |   9
-b.
+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. What are Bag of Word (BOVW) and Bag of Visual Word (BOW)? Are they synonyms?
+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?
-Q4. How do they apply BOVW to compute representation of a word (concept) from a large set of Images?
+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"
-Q5. Have you ever wish to see a beautiful Mermaids.
+Think about a computational way to show that how they look like?
-Have you ever seen "Unicorns" in the real lie.
-Can you think a computational way to show that how they look like?

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