Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)
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| Challenge: | Named entity recognition (NER) is a key task reliant on textual data. |
| Approach: | They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries. |
| Outcome: | The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets. |
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| Challenge: | Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations. |
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| Challenge: | Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks. |
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| Challenge: | Named entity recognition (NER) is a task to identify textual spans that correspond to named entities in the given text. |
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| Challenge: | Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task. |
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| Challenge: | Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors. |
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| Challenge: | Named entity recognition (NER) is a fundamental task in the field of information extraction and has played an important role in the development of natural language processing. |
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| Challenge: | Named Entity Recognition and Relation Extraction are interdependent tasks in information extraction. |
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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
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