Hai Jin


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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.


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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