no code implementations • 31 Jan 2024 • Xiaopeng Li, Shasha Li, Shezheng Song, Huijun Liu, Bin Ji, Xi Wang, Jun Ma, Jie Yu, Xiaodong Liu, Jing Wang, Weimin Zhang
In particular, local editing methods, which directly update model parameters, are more suitable for updating a small amount of knowledge.
no code implementations • 23 Oct 2022 • Bin Ji, Shasha Li, Hao Xu, Jie Yu, Jun Ma, Huijun Liu, Jing Yang
On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE.
Joint Entity and Relation Extraction named-entity-recognition +3
no code implementations • 18 Aug 2022 • Bin Ji, Hao Xu, Jie Yu, Shasha Li, Jun Ma, Yuke Ji, Huijun Liu
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.
no code implementations • 17 Aug 2022 • Huijun Liu, Jie Yu, Shasha Li, Jun Ma, Bin Ji
Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness.
no code implementations • COLING 2022 • Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu
Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples.
no code implementations • 11 Jul 2022 • Mengxue Du, Shasha Li, Jie Yu, Jun Ma, Bin Ji, Huijun Liu, Wuhang Lin, Zibo Yi
Document retrieval enables users to find their required documents accurately and quickly.
no code implementations • 7 Jul 2022 • Bin Ji, Shasha Li, Jie Yu, Jun Ma, Huijun Liu
Previous research has demonstrated that the two paradigms have clear complementary advantages, but few models have attempted to leverage these advantages in a single NER model as far as we know.
no code implementations • 19 Nov 2021 • Huijun Liu, Chunhua Yang, Ao Li, Sheng Huang, Xin Feng, Zhimin Ruan, Yongxin Ge
In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available.
no code implementations • 21 May 2021 • Bin Ji, Shasha Li, Jie Yu, Jun Ma, Huijun Liu
To solve this problem, we pro-pose Sequence Tagging enhanced Span-based Network (STSN), a span-based joint extrac-tion network that is enhanced by token BIO label information derived from sequence tag-ging based NER.
Joint Entity and Relation Extraction named-entity-recognition +4
no code implementations • COLING 2020 • Bin Ji, Jie Yu, Shasha Li, Jun Ma, Qingbo Wu, Yusong Tan, Huijun Liu
Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction.
1 code implementation • 25 Jun 2018 • Ziyue Zhao, Huijun Liu, Tim Fingscheidt
Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors, is a challenging task.