Sentiment Tagging with Partial Labels using Modular Architectures
Table 2: Comparing our models with recent results on the Aspect Sentiment datasets.
['Models', 'English E+A', 'English E+A+S', 'Spanish E+A', 'Spanish E+A+S', 'Dutch E+A', 'Dutch E+A+S', 'Russian E+A', 'Russian E+A+S']
S6SS2SSS0Px2 Aspect Based Sentiment We evaluated our models on two tasks: The first uses two modules, for identifying the position of the aspect in the text (i.e., chunking) and the aspect category prediction (denoted E+A). The second adds a third module that predicts the sentiment polarity associated with the aspect (denoted E+A+S). I.e., for a given sentence, label its entity span, the aspect category of the entity and the sentiment polarity of the entity at the same time. The results over four languages are summarized in Tab. In all cases, our modular approach outperforms all monolithic approaches.