Ping Zhang


2021

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Contrastive Attention for Automatic Chest X-ray Report Generation
Fenglin Liu | Changchang Yin | Xian Wu | Shen Ge | Ping Zhang | Xu Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Document Classification for COVID-19 Literature
Bernal Jiménez Gutiérrez | Juncheng Zeng | Dongdong Zhang | Ping Zhang | Yu Su
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset. We find that pre-trained language models outperform other models in both low and high data regimes, achieving a maximum F1 score of around 86%. We note that even the highest performing models still struggle with label correlation, distraction from introductory text and CORD-19 generalization. Both data and code are available on GitHub.

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Document Classification for COVID-19 Literature
Bernal Jimenez Gutierrez | Jucheng Zeng | Dongdong Zhang | Ping Zhang | Yu Su
Findings of the Association for Computational Linguistics: EMNLP 2020

The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 23,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that BioBERT surpasses the others by a small margin with micro-F1 and accuracy scores of around 86% and 75% respectively on the test set. We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis. We perform a data ablation study to determine how important article titles are for achieving reasonable performance on this dataset. Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work. Both data and code are available on GitHub.

2008

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Chinese Word Sense Disambiguation with PageRank and HowNet
Jinghua Wang | Jianyi Liu | Ping Zhang
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

2006

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A Natural Language Model of Computing with Words in Web Pages
Ze-yu Zheng | Ping Zhang
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation