CityU-DAC: Disambiguating Sentiment-Ambiguous Adjectives within Context
Bin Lu, Benjamin K. Tsou · 2010
This paper describes our system participating in task 18 of SemEval-2010, i.e. disambiguating Sentiment-Ambiguous Adjectives (SAAs). To disambiguating SAAs, we compare the machine learning-based and lexiconbased methods in our submissions: 1) Maximum entropy is used to train classifiers based on the annotated Chinese data from the NTCIR opinion analysis tasks, and the clause-level and sentence-level classifiers are compared; 2) For the lexicon-based method, we first classify the adjectives into two classes: intensifiers (i.e. adjectives intensifying the intensity of context) and suppressors (i.e. adjectives decreasing the intensity of context), and then use the polarity of context to get the SAAs ’ contextual polarity based on a sentiment lexicon. The results show that the performance of maximum entropy is not quite high due to little training data; on the other hand, the lexicon-based method could improve the precision by considering the polarity of context. 1