Pan Du


2021

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Inductive Topic Variational Graph Auto-Encoder for Text Classification
Qianqian Xie | Jimin Huang | Pan Du | Min Peng | Jian-Yun Nie
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Graph convolutional networks (GCNs) have been applied recently to text classification and produced an excellent performance. However, existing GCN-based methods do not assume an explicit latent semantic structure of documents, making learned representations less effective and difficult to interpret. They are also transductive in nature, thus cannot handle out-of-graph documents. To address these issues, we propose a novel model named inductive Topic Variational Graph Auto-Encoder (T-VGAE), which incorporates a topic model into variational graph-auto-encoder (VGAE) to capture the hidden semantic information between documents and words. T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE. It learns probabilistic representations of words and documents by jointly encoding and reconstructing the global word-level graph and bipartite graphs of documents, where each document is considered individually and decoupled from the global correlation graph so as to enable inductive learning. Our experiments on several benchmark datasets show that our method outperforms the existing competitive models on supervised and semi-supervised text classification, as well as unsupervised text representation learning. In addition, it has higher interpretability and is able to deal with unseen documents.

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Graph Relational Topic Model with Higher-order Graph Attention Auto-encoders
Qianqian Xie | Jimin Huang | Pan Du | Min Peng
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2018

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Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection
Pan Du | Jian-Yun Nie
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes MuTuX, our system that is designed for task 1-5a, emotion classification analysis of tweets on SemEval2018. The system aims at exploring the potential of context information of terms for emotion analysis. A Recurrent Neural Network is adopted to capture the context information of terms in tweets. Only term features and the sequential relations are used in our system. The results submitted ranks 16th out of 35 systems on the task of emotion detection in English-language tweets.