Nested Named Entity Recognition via Second-best Sequence Learning and Decoding (2020.tacl-1)
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| Challenge: | Named entity recognition (NER) is the task of identifying text spans associated with proper names and classifying them according to their semantic class such as person or organization. |
| Approach: | They propose a method that treats the tag sequence for nested entities as the second best path within the span of their parent entity. |
| Outcome: | The proposed method achieves F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE 2005, and GENIA datasets. |
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| Challenge: | Existing methods for named entity recognition assume entities are not nested within other entities, so-called flat NER. |
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
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| Challenge: | nested named entity recognition is a sequence labeling task that can be trained using any off-the-shelf sequence labelling library. |
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Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. |
| Approach: | They propose to revisit the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type. |
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A Neural Layered Model for Nested Named Entity Recognition (N18-1)
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| Challenge: | Entity mentions embedded in longer entity mentions are referred to as nested entities due to the properties of natural language. |
| Approach: | They propose a neural model that dynamically stacks flat NER layers to identify nested entities by capturing sequential context representation with bidirectional long-term memory. |
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Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)
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| Challenge: | Named entity recognition (NER) is a well-studied task in natural language processing. |
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