1 code implementation • 15 Jun 2021 • 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
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on Named Entity Recognition on CMeEE
Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
Ranked #3 on Relation Extraction on DocRED
In order to accelerate the research for domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a high-quality Chinese dataset for Diabetes knowledge graph, which contains 22, 050 entities and 6, 890 relations in total.
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
We introduce a Poincare probe, a structural probe projecting these embeddings into a Poincare subspace with explicitly defined hierarchies.
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks.
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots.
With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes.
Ranked #4 on Nested Named Entity Recognition on ACE 2004
In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i. e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population.
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #6 on Relation Extraction on NYT
Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus.
Ranked #3 on Hypernym Discovery on General