Qing Wang


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Emotion Classification by Jointly Learning to Lexiconize and Classify
Deyu Zhou | Shuangzhi Wu | Qing Wang | Jun Xie | Zhaopeng Tu | Mu Li
Proceedings of the 28th International Conference on Computational Linguistics

Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures – LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.


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Domain Adaptation for Low-Resource Neural Semantic Parsing
Alvin Kennardi | Gabriela Ferraro | Qing Wang
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association


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MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews
Wei Xue | Wubai Zhou | Tao Li | Qing Wang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated reviews. However, the two tasks are often treated independently, even though they are closely related. Intuitively, the learned knowledge of one task should inform the other learning task. In this paper, we propose a multi-task learning model based on neural networks to solve them together. We demonstrate the improved performance of our multi-task learning model over the models trained separately on three public dataset released by SemEval workshops.