Ranran Haoran Zhang


COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang | Manling Li | Xuan Wang | Nikolaus Parulian | Guangxing Han | Jiawei Ma | Jingxuan Tu | Ying Lin | Ranran Haoran Zhang | Weili Liu | Aabhas Chauhan | Yingjun Guan | Bangzheng Li | Ruisong Li | Xiangchen Song | Yi Fung | Heng Ji | Jiawei Han | Shih-Fu Chang | James Pustejovsky | Jasmine Rah | David Liem | Ahmed ELsayed | Martha Palmer | Clare Voss | Cynthia Schneider | Boyan Onyshkevych
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.


Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
Ranran Haoran Zhang | Qianying Liu | Aysa Xuemo Fan | Heng Ji | Daojian Zeng | Fei Cheng | Daisuke Kawahara | Sadao Kurohashi
Findings of the Association for Computational Linguistics: EMNLP 2020

Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order on the unordered triplets and involves a large decoding length associated with error accumulation. These methods introduce exposure bias, which may cause the models overfit to the frequent label combination, thus limiting the generalization ability. We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets. We evaluate our model on two datasets, DuIE and NYT, and systematically study how exposure bias alters the performance of Seq2Seq models. Experiments show that the state-of-the-art Seq2Seq model overfits to both datasets while Seq2UMTree shows significantly better generalization. Our code is available at https://github.com/WindChimeRan/OpenJERE.