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Aspect Based Sentiment Analysis Task

Slot1
- Given: sentence
- Predict: E#A labels sentence
- ex: "The food is expensive but service is good." => FOOD#PRICE, SERVICE#QUALITY
Slot2
- Given: sentence and E#A label
- Predict: opinion target
- ex: "The food is delicious", where is the FOOD#QUALITY? => "food"
Slot3
- Given: sentence, E#A label
- Predict: polarity
- ex: "The food is delicious", what is the polarity for "food" / FOOD#QUALITY? => "positive"

Slot1
- task: Multi-label
- feature: 1,000 most frequent unigrams of the training data excluding stop-words
- model: SVM (linear kernel)
  - the label that over threshold prob is assigned (threshold = 0.2).
- evaluation: f1 (micro-averaging)
Slot2
- task: Target Detection
- model: dictionary ({"category": "word"}), it is made from opinions of training data. Search category by using dictionary, and the first match one is used. If the target does not exist, return the NULL.
- evaluation: f1 (micro-averaging), discards NULL targets.
Slot3:
- task: Sentiment (0/1 prediction)
- feature: 1,000 most frequent unigrams of the training data excluding stop-words + index of E&A cateogry
- accuracy (number of correctly predicted polarity of the (gold) aspect categories / total number of the gold aspect categories)

Baseline scores

Best model Score