MixTEA: Semi-supervised Entity Alignment with Mixture Teaching (2023.findings-emnlp)
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| Challenge: | Existing methods to learn informative entity embeddings are insufficient for semi-supervised entity alignment. |
| Approach: | They propose a semi-supervised method which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings. |
| Outcome: | The proposed method is superior to existing methods on benchmark datasets and further analyses. |
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From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (2021.emnlp-main)
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| Challenge: | Existing methods for cross-lingual entity alignment rely on lexical matching and probability reasoning, but they inherit poor interpretability and low efficiency from neural networks. |
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ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)
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Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model (D19-1)
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| Challenge: | Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages. |
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Deep Reinforcement Learning for Entity Alignment (2022.findings-acl)
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| Challenge: | Entity alignment (EA) methods identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. |
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Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)
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Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
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