1 code implementation • ACL 2021 • Zijun Yao, Chengjiang Li, Tiansi Dong, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Yichi Zhang, Zelin Dai
Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts.
no code implementations • 14 Jul 2020 • Tiansi Dong, Chengjiang Li, Christian Bauckhage, Juanzi Li, Stefan Wrobel, Armin B. Cremers
In contrast to traditional neural network, ENN can precisely represent all 24 different structures of Syllogism.
1 code implementation • IJCNLP 2019 • Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua
Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities.
1 code implementation • ACL 2019 • Yixin Cao, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments.
Ranked #30 on Entity Alignment on DBP15k zh-en
no code implementations • EMNLP 2018 • Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong
Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings.
no code implementations • IJCNLP 2017 • Liangming Pan, Xiaochen Wang, Chengjiang Li, Juanzi Li, Jie Tang
Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education.
no code implementations • IJCNLP 2017 • Yixin Cao, Jiaxin Shi, Juanzi Li, Zhiyuan Liu, Chengjiang Li
To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model.
no code implementations • ACL 2017 • Liangming Pan, Chengjiang Li, Juanzi Li, Jie Tang
What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares?