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courses:rg:2012:atreport [2012/06/03 20:57] tamchyna vytvořeno |
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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 | ||
Line 28: | Line 28: | ||
- 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: | - overfitting: | ||
- | 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/ | - identify the set of word pairs that can be hyper/ | ||
- 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 " | - the " | ||
- | 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' | - what is MiniPar? very simple parser (that' |