Abstract
The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual’s salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation. Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar’s test, p <0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature. (0.426 F1 vs 0.413 F1)- Anthology ID:
- 2021.findings-emnlp.311
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3671–3681
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.311
- DOI:
- 10.18653/v1/2021.findings-emnlp.311
- Cite (ACL):
- Hitarth Narvala, Graham McDonald, and Iadh Ounis. 2021. RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3671–3681, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification (Narvala et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.311.pdf