Papers by Shimei Pan
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) are used for diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question/Answering. |
| Approach: | They propose to add medical knowledge to pre-trained language models to facilitate clinical relation extraction using a large text corpus. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset. |
Supervising Unsupervised Open Information Extraction Models (D19-1)
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| Challenge: | Existing supervised methods that use labeled training data are expensive and difficult to adapt to new domains. |
| Approach: | They propose a supervised open information extraction framework that leverages unsupervised Open IE systems and labeled data to improve system performance. |
| Outcome: | The proposed method outperforms existing supervised and unsupervised models by a significant margin. |
Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios (2022.lrec-1)
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| Challenge: | Psycholinguistic knowledge resources have been widely used in constructing features for text-based human trait and behavior analysis. |
| Approach: | They propose to incorporate a widely-used psycholinguistic lexicon into NN models to improve human trait and behavior analysis in low resource scenarios. |
| Outcome: | The proposed methods perform significantly better than baselines that use only LIWC or NN-based feature learning methods. |
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models (2025.acl-long)
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Tao Zhang, Ziqian Zeng, YuxiangXiao YuxiangXiao, Huiping Zhuang, Cen Chen, James R. Foulds, Shimei Pan
| Challenge: | Large Language Models (LLMs) generate content that exhibits gender biases, raising ethical concerns. |
| Approach: | They propose to use a dataset to identify gender biases in Large Language Models (LLMs) this dataset is a "chosen" and "rejected" LLM alignment is an effective approach to mitigate gender bias. |
| Outcome: | The proposed dataset shows that it reduces gender bias and improves quality. |
Predicting Malware Attributes from Cybersecurity Texts (N19-1)
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| Challenge: | a new feature learning method is proposed to automatically assign malware attribute labels based on cybersecurity texts. |
| Approach: | They propose a feature learning method to leverage diverse knowledge sources to automatically assign malware attribute labels based on cybersecurity texts. |
| Outcome: | The proposed method outperforms the state-of-the-art malware attribute prediction systems. |