no code implementations • Findings (ACL) 2022 • Hailong Jin, Tiansi Dong, Lei Hou, Juanzi Li, Hui Chen, Zelin Dai, Qu Yincen
Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.
no code implementations • 22 Mar 2024 • Tiansi Dong, Mateja Jamnik, Pietro Liò
SphNN is the first neural model that can determine the validity of long-chained syllogistic reasoning in one epoch by constructing sphere configurations as Euler diagrams, with the worst computational complexity of O(N^2).
no code implementations • 25 Jul 2023 • Tiansi Dong, Rafet Sifa
The core of our methodology is a neurosymbolic sense embedding, in terms of a configuration of nested balls in n-dimensional space.
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.
no code implementations • IJCNLP 2019 • Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong
This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base.
2 code implementations • ICLR 2019 • Tiansi Dong, Olaf Cremers, Hailong Jin, Juanzi Li, Chrisitan Bauckhage, Armin B. Cremers, Daniel Speicher, Joerg Zimmermann
Experiment results also show that $n$-ball embeddings demonstrate surprisingly good performance in validating the category of unknown word.
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.
1 code implementation • COLING 2018 • Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong
Fine-grained entity typing aims at identifying the semantic type of an entity in KB.