The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
Open accessAbstract
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model ( Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors.The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to ERNIE (Zhang et al., 2019) and KnowBert (Peters et al., 2019), but it requires no expensive further pretraining of the BERT encoder.We evaluate E-BERT on unsupervised question answering (QA), supervised relation classification (RC) and entity linking (EL).On all three tasks, E-BERT outperforms BERT and other baselines.We also show quantitatively that the original BERT model is overly reliant on the surface form of entity names (e.g., guessing that someone with an Italian-sounding name speaks Italian), and that E-BERT mitigates this problem.
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“The ability of Large Language Models to retrieve factual knowledge as explicit knowledge bases [53-56, 56-61] has been stirred discussed, which presents an opportunity to construct more comprehensive knowledge graphs within recommender systems.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [58]
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Self-citationIntroduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Self-citationXLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687
Exploiting Similarities among Languages for Machine TranslationPreprint
Mikolov, T., Le, Q. V., & Sutskever, I. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.4168 · Cited by 1,728
ERNIE: Enhanced Language Representation with Informative Entities
Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1139 · Cited by 1,482
Fixing Weight Decay Regularization in Adam
Loshchilov, I., & Hutter, F. (2018) · Cited by 1,297
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AKER: Arabic Knowledge-enriched Reader for Machine Reading Comprehension
Albilali, E., Al-Twairesh, N., & Hosny, M. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3736164 · Cited by 3
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models
Barmettler, J., Bernstein, A., & Rossetto, L. (2025) · arXiv · doi:10.48550/arxiv.2504.07624 · Cited by 3
Metadata
- Authors
- Poerner, N., Waltinger, U., & Schütze, H.
- Affiliations
- Siemens (Germany) (DE)
- Ludwig-Maximilians-Universität München (DE)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 803-818
- OpenAlex
- W3021191241
- Cited by
- 146
- References
- 33
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 21 · 2023: 24 · 2022: 34 · 2021: 34 · 2020: 15
- FWCI
- 10.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Siemens
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- entity embeddings, BERT, knowledge injection, relation classification, entity linking
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