Qinghua Zheng


Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning
Wenbin An | Feng Tian | Ping Chen | Siliang Tang | Qinghua Zheng | QianYing Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of the same granularity.In this paper, we investigate a new practical scenario called Fine-grained Category Discovery under Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained categories with only coarse-grained labeled data, which can adapt models to categories of different granularity from known ones and reduce significant labeling cost. It is also a challenging task since supervised training on coarse-grained categories tends to focus on inter-class distance (distance between coarse-grained classes) but ignore intra-class distance (distance between fine-grained sub-classes) which is essential for separating fine-grained categories.Considering most current methods cannot transfer knowledge from coarse-grained level to fine-grained level, we propose a hierarchical weighted self-contrastive network by building a novel weighted self-contrastive module and combining it with supervised learning in a hierarchical manner.Extensive experiments on public datasets show both effectiveness and efficiency of our model over compared methods.

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Shangbin Feng | Zhaoxuan Tan | Zilong Chen | Ningnan Wang | Peisheng Yu | Qinghua Zheng | Xiaojun Chang | Minnan Luo
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic ideological analysis. In this paper, we propose PAR, a Political Actor Representation learning framework that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train PAR with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction. Further analysis proves that PAR learns representations that reflect the political reality and provide new insights into political behavior.


基于有向异构图的发票明细税收分类方法(Tax Classification of Invoice Details Based on Directed Heterogeneous Graph)
Peiyao Zhao (赵珮瑶) | Qinghua Zheng (郑庆华) | Bo Dong (董博) | Jianfei Ruan (阮建飞) | Minnan Luo (罗敏楠)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

税收是国家赖以生存的物质基础。为加快税收现代化,方便纳税人便捷、规范开具增值税发票,国税总局规定纳税人在税控系统开票前选择发票明细对应的税收分类才可正常开具发票。提高税收分类的准确度,是构建税收风险指标和分析纳税人行为特征的重要基础。基于此,本文提出了一种基于有向异构图的短文本分类模型(Heterogeneous Directed Graph Attenton Network,HDGAT),利用发票明细间的有向信息建模,引入外部知识,显著地提高了发票明细的税收分类准确度。


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A Segmentation Matrix Method for Chinese Segmentation Ambiguity Analysis
Yanping Chen | Qinghua Zheng | Feng Tian | Deli Zheng
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 1, June 2016


Omni-word Feature and Soft Constraint for Chinese Relation Extraction
Yanping Chen | Qinghua Zheng | Wei Zhang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation
Zhenhua Tian | Hengheng Xiang | Ziqi Liu | Qinghua Zheng
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)