CityU-DAC: Disambiguating Sentiment-Ambiguous Adjectives within Context

This paper describes our system participating in task 18 of SemEval-2010, i.e. disambiguating SentimentAmbiguous 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.