activity
20202023
most citedDomain-Specific NER via Retrieving Correlated Samples

9 citations · 20 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CL2023

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks

Xuming Hu, Aiwei Liu, Zeqi Tan +4

Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training.…

cs.CL2022

Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction

Xuming Hu, Shiao Meng, Chenwei Zhang +4

Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NE…

cs.CL2022

Query-based Instance Discrimination Network for Relational Triple Extraction

Zeqi Tan, Yongliang Shen, Xuming Hu +4

Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a ste…

cs.CL2022

Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution

Aiwei Liu, Honghai Yu, Xuming Hu +5

We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split int…

cs.CL20229 cited

Domain-Specific NER via Retrieving Correlated Samples

Xin Zhang, Yong Jiang, Xiaobin Wang +4

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requir…

cs.CV2022

Scene Graph Modification as Incremental Structure Expanding

Xuming Hu, Zhijiang Guo, Yu Fu +2

A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene. Scene graphs play an important role in many cross m…