Abstract:In the current aspect extraction researches, the attention modeling and training are fixed, and the sentence is modeled in one time step. However, the semantics of the words vary in contexts, and a fixed attention distribution lacks dynamic adaptability. Therefore, a gated dynamic attention mechanism towards aspect extraction is proposed in this paper. A bidirectional long short term memory network is exploited to obtain hidden representations of words in a target sentence. Then, a specific attention distribution is computed according to the target word and its context while the attention model labelling words. Thus, the attention-weight distribution can be automatically adjusted according to the changes of contexts. Next, a gate is adopted to adjust the quantities of information flowing to the next units. Finally, conditional random field is utilized to label the aspect. The official datasets of 2014-2016 semantic evaluation are employed to verify the effectiveness of the proposed method, and F1 scores are increased.
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