Pre-trained Language Models for Entity Blocking: A Reproducibility Study (2024.naacl-long)
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| Challenge: | Entity Resolution (ER) is an essential task in data integration . state-of-the-art neural IR models that are based on large language models have not been evaluated on the ER task. |
| Approach: | They evaluate state-of-the-art neural IR models that are based on large language models on a wide range of real-world datasets and evaluate their generalization abilities. |
| Outcome: | The proposed methods have been evaluated on a wide range of datasets and their generalization abilities. |
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
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ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
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| Challenge: | Existing approaches to entity resolution focus on supervised learning, but manual annotation is labor-intensive. |
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| Challenge: | a proposed entity linking model that disjointly applies MD and ED from the same contextualized BERT embeddings is able to generalize better. |
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| Challenge: | Existing Entity typing models suffer from noisy labels due to distant supervision . |
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Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching (2025.coling-main)
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| Challenge: | Entity matching (EM) is a critical step in entity resolution (ER). |
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DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
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| Challenge: | Entity recognition is a widely benchmarked task in natural language processing . a neural architecture called BiLSTM-CRF is used to model the language sequences . |
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Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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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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