Papers by Mosha Chen
OntoED: Low-resource Event Detection with Ontology Embedding (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to ED rely on training instances and ignore correlation of event types. |
| Approach: | They propose a process of event ontology population linking event instances to pre-defined event types in event ontoology and ontological embedding to address these problems. |
| Outcome: | The proposed framework can be applied to new unseen event types by establishing linkages to existing ones. |
Predicting Clinical Trial Results by Implicit Evidence Integration (2020.emnlp-main)
Copied to clipboard
| Challenge: | Clinical trials are expensive and time-consuming, and inappropriately designed studies can be devastating in a pandemic. |
| Approach: | They propose a model that takes a PICO-formatted clinical trial proposal and predicts the outcome from it. |
| Outcome: | The proposed model outperforms baseline models on a benchmark dataset with 10.7% relative gain over BioBERT. |
Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises. |
| Approach: | They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions. |
| Outcome: | The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance. |
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
Copied to clipboard
Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)
Copied to clipboard
Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
| Challenge: | a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages. |
| Approach: | They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models. |
| Outcome: | The proposed benchmarks show that the current models perform worse than the human ceiling. |