Chunping Li


Discovering Topics in Long-tailed Corpora with Causal Intervention
Xiaobao Wu | Chunping Li | Yishu Miao
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder
Xiaobao Wu | Chunping Li | Yan Zhu | Yishu Miao
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic models have been prevailing for many years on discovering latent semantics while modeling long documents. However, for short texts they generally suffer from data sparsity because of extremely limited word co-occurrences; thus tend to yield repetitive or trivial topics with low quality. In this paper, to address this issue, we propose a novel neural topic model in the framework of autoencoding with a new topic distribution quantization approach generating peakier distributions that are more appropriate for modeling short texts. Besides the encoding, to tackle this issue in terms of decoding, we further propose a novel negative sampling decoder learning from negative samples to avoid yielding repetitive topics. We observe that our model can highly improve short text topic modeling performance. Through extensive experiments on real-world datasets, we demonstrate our model can outperform both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics.


Combining Context Features by Canonical Belief Network for Chinese Part-Of-Speech Tagging
Hongzhi Xu | Chunping Li
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II