Hang Gao


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Multi-Label Few-Shot Learning for Aspect Category Detection
Mengting Hu | Shiwan Zhao | Honglei Guo | Chao Xue | Hang Gao | Tiegang Gao | Renhong Cheng | Zhong Su
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD task since a sentence may contain multiple aspect categories. Therefore, we propose a multi-label few-shot learning method based on the prototypical network. To alleviate the noise, we design two effective attention mechanisms. The support-set attention aims to extract better prototypes by removing irrelevant aspects. The query-set attention computes multiple prototype-specific representations for each query instance, which are then used to compute accurate distances with the corresponding prototypes. To achieve multi-label inference, we further learn a dynamic threshold per instance by a policy network. Extensive experimental results on three datasets demonstrate that the proposed method significantly outperforms strong baselines.

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Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement
Mengting Hu | Honglei Guo | Shiwan Zhao | Hang Gao | Zhong Su
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and be easier to understand. Given a document, the existing automatic mind-map generation method extracts the relationships of every sentence pair to generate the directed semantic graph for this document. The computation complexity increases exponentially with the length of the document. Moreover, it is difficult to capture the overall semantics. To deal with the above challenges, we propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. To guarantee a meaningful mind-map, we design a graph refinement module to adjust the relation graph in a reinforcement learning manner. Extensive experimental results demonstrate that the proposed approach is more effective and efficient than the existing methods. The inference time is reduced by thousands of times compared with the existing methods. The case studies verify that the generated mind-maps better reveal the underlying semantic structures of the document.


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MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao | Tim Oates
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams
Taneeya Satyapanich | Hang Gao | Tim Finin
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)