RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification (2021.findings-emnlp)
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| Challenge: | Existing relationships between entities can be reliable indicators for classifying sensitive information, such as commercially sensitive information. |
| Approach: | They propose to represent entities and relations within a single embedding to better capture the relationship between the entities. |
| Outcome: | The proposed method significantly improves the effectiveness of sensitivity classification compared to existing methods. |
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
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| Challenge: | Existing knowledge graph embedding methods fail to solve two major problems at the same time, leading to unsatisfactory results. |
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| Challenge: | Knowledge graph embedding is a new form of knowledge graphing that allows for better link prediction. |
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| Challenge: | Existing methods for pre-trained language models lack explicit grounding in real-world entities. |
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