| Challenge: | Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks. |
| Approach: | They propose a multi-task neural model to jointly extract entities and negations from medical reports. |
| Outcome: | The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset. |
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models. |
| Approach: | They propose to integrate CVAE into a span-based Named Entity Recognition model. |
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An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)
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| Challenge: | Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks. |
| Approach: | They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way. |
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A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
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A Unified MRC Framework for Named Entity Recognition (2020.acl-main)
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| Challenge: | Named entity recognition is divided into nested NER and flat NER depending on whether entities are nesting. |
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T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates (2023.tacl-1)
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| Challenge: | Named Entity Recognition (NER) has evolved from flat to overlapped and discontinuous . NER is a text recognition task that recognizes mentions that represent entities in text . |
| Approach: | They propose a two-stage span-based framework to solve a unified NER task using two stages . they extract entity spans, classify over all entity span pairs and combine them to train two stages. |
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Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework (2025.naacl-long)
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| Challenge: | Recent studies have demonstrated that large language models (LLMs) can perform in named entity recognition tasks. |
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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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Merge and Label: A Novel Neural Network Architecture for Nested NER (P19-1)
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| Challenge: | Named entity recognition (NER) is one of the best studied tasks in natural language processing. |
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Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)
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| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. |
| Approach: | They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages. |
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A Hybrid Detection and Generation Framework with Separate Encoders for Event Extraction (2023.eacl-main)
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| Challenge: | Recent work on event extraction tasks has been based on classification-based methods . a new generation-based method is being developed to extract event triggers and event arguments from plain text. |
| Approach: | They propose to use independent encoders to model event detection and event argument extraction, respectively, and use token-level features to precisely control the fusion between two encoder. |
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