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courses:rg:2012:atreport [2012/06/03 20:57]
tamchyna vytvořeno
courses:rg:2012:atreport [2012/06/03 21:01] (current)
tamchyna added report, fixed format
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 - looking for the taxonomy that maximizes its likelihood given the evindence - looking for the taxonomy that maximizes its likelihood given the evindence
  
-2.3+==== 2.3 ====
 - describes the main part of the algorithm - describes the main part of the algorithm
 - start with an initial taxonomy, add relations - start with an initial taxonomy, add relations
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 - adding a relation involves adding relations implied by transitivity - adding a relation involves adding relations implied by transitivity
  
-2.4+==== 2.4 ====
 - model adaptation for ambiguity - model adaptation for ambiguity
 - adding relations between word senses - adding relations between word senses
  
-Section 3+===== Section 3 =====
  
-3.1+==== 3.1 ==== 
 - evidence is a vector - evidence is a vector
 - training data from WordNet, trained a classifier using logistic regression - training data from WordNet, trained a classifier using logistic regression
 - overfitting: don't want to model too long-distance relations - overfitting: don't want to model too long-distance relations
  
-3.2+==== 3.2 ====
 - cousins - cousins
 - clustering -- similarity is cosine distance (within the cluster, 0 otherwise) - clustering -- similarity is cosine distance (within the cluster, 0 otherwise)
 - softmax regression: more than 2 classes, otherwise similar to logistic regression - softmax regression: more than 2 classes, otherwise similar to logistic regression
  
-3.3+==== 3.3 ====
 - identify the set of word pairs that can be hyper/hyponyms - identify the set of word pairs that can be hyper/hyponyms
 - for each proposed hypernym... was explained - for each proposed hypernym... was explained
 - add the one with highest score according to the classifiers - add the one with highest score according to the classifiers
  
-3.4+==== 3.4 ====
 - sense disambiguation - sense disambiguation
 - works by itself - works by itself
 - the "carrier" example explained - the "carrier" example explained
  
-Evaluation+===== Evaluation =====
 - manual evaluation -- uniformly generated samples from the first n links, human judge - manual evaluation -- uniformly generated samples from the first n links, human judge
 - annotators were to classify into 4 classes (4.1) - annotators were to classify into 4 classes (4.1)
 - all the various evaluation methods discussed - all the various evaluation methods discussed
  
-DISCUSSION:+===== DISCUSSION =====
  
 - what is MiniPar? very simple parser (that's the reason it was - what is MiniPar? very simple parser (that's the reason it was
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 - features are both the dependency labels but they are also lexicalized: - features are both the dependency labels but they are also lexicalized:
  
 +<code>
     vehicle     vehicle
    /       \ A    /       \ A
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              /              /
             a             a
 +</code>
  
 results in features? maybe "vehicle, car ... A -> as -> B" ? results in features? maybe "vehicle, car ... A -> as -> B" ?

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