Papers with Entity disambiguation
ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation (2023.eacl-main)
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| Challenge: | Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base. |
| Approach: | They propose a benchmark for entity disambiguation that includes a unified training data set, entity vocabulary, candidate lists and challenging evaluation splits covering 8 different domains. |
| Outcome: | The proposed benchmark is based on a unified training data set, entity vocabulary, candidate lists and evaluation splits covering 8 different domains. |
Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)
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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li
| Challenge: | Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. |
| Approach: | They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. |
| Outcome: | The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks. |
Entity Embedding Completion for Wide-Coverage Entity Disambiguation (2022.findings-emnlp)
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| Challenge: | Existing state-of-the-art ED models do not address out-of vocabulary entities that are absent from training data. |
| Approach: | They propose to extend a state-of-the-art ED model by dynamically computing embeddings of out-ofvocabulary entities by using entity descriptions and mention contexts. |
| Outcome: | The proposed model performs comparable to existing models whose embeddings are trained for all candidate entities as well as embedd-free models. |
Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation (2025.acl-long)
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| Challenge: | Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base. |
| Approach: | They propose a document-level Dual Encoder model that embeds mentions and label candidates in a shared embedding space and applies a similarity metric to predict the correct label. |
| Outcome: | The proposed model improves the disambiguation of ambiguous mentions of entities in text to their respective KB entries. |
Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing (2021.emnlp-main)
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| Challenge: | Entity disambiguation (ED) is the last step of entity linking when candidate entities are reranked according to the context they appear in. |
| Approach: | They propose a dataset that includes 16K short text snippets annotated with entity mentions to evaluate EL models. |
| Outcome: | The proposed dataset shows that the performance of EL systems is overestimated . the results show that the EL system performance is significantly better on the ShadowLink benchmark . |
Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning (2024.emnlp-main)
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Panuthep Tasawong, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Entity disambiguation (ED) is crucial in natural language processing tasks such as question-answering and information extraction. |
| Approach: | They propose a method to reduce computational overhead on overshadowed entities by addressing shortcut learning. |
| Outcome: | The proposed method achieves state-of-the-art performance without compromising inference speed. |