Hai Jin


2022

pdf bib
OpticE: A Coherence Theory-Based Model for Link Prediction
Xiangyu Gui | Feng Zhao | Langjunqing Jin | Hai Jin
Proceedings of the 29th International Conference on Computational Linguistics

Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector or a point in a feature space. The distance between these points is a measure of semantic similarity. However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others. It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities. To alleviate the semantic ambiguity in KGs, we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference. It is a lightweight and relation-adaptive model based on coherence theory, in which each entity’s semantics vary automatically regarding different relations. In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process. The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods.

2021

pdf bib
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
Zhexue Chen | Hong Huang | Bang Liu | Xuanhua Shi | Hai Jin
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021