| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. |
| Approach: | They propose a method to handle both types of NEs in one system by using a biaffine dependency parsing model which scores pairs of start and end tokens in a sentence. |
| Outcome: | The proposed model performs well on 8 corpora and achieves accuracy gains of up to 2.2 percentage points. |
Similar Papers
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. |
| Approach: | They propose to formulate named entity recognition task as machine reading comprehension task instead of sequence labeling problem . |
| Outcome: | The proposed framework achieves vast amount of performance boost over current models on nested and flat NER datasets. |
Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
| Approach: | They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact. |
| Outcome: | The proposed model achieves better performance than previous approaches while requiring fewer parameters. |
Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)
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| Challenge: | Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text. |
| Approach: | They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation. |
| Outcome: | The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities. |
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. |
| Outcome: | The proposed model achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models. |
Joint Learning of Named Entity Recognition and Entity Linking (P19-2)
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| Challenge: | Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected . |
| Approach: | They perform joint learning of named entity recognition and entity linking to leverage their relatedness. |
| Outcome: | The proposed model achieves competitive results with the state-of-the-art in both NER and EL tasks. |
Learning Nested Named Entity Recognition from Flat Annotations (2026.eacl-srw)
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| Challenge: | Named entity recognition (NER) requires expensive multi-level annotation. |
| Approach: | They evaluate four approaches to learning nested structure from flat annotations alone . on NEREL, a Russian benchmark, they find the best method achieves 26.37% inner F1 . |
| Outcome: | The proposed method closes 40% of the gap to full nested supervision on a Russian benchmark with 29 entity types where 21% of entities are nest. |
Bipartite Flat-Graph Network for Nested Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing models only consider the unidirectional delivery of information from innermost layers to outer ones, but instead focus on nested entities. |
| Approach: | They propose a bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER) the bipartites are bidirectional LSTM and graph convolutional network (GCN) they first use the entities recognized by the flat NER module to construct an entity graph . |
| Outcome: | The proposed model outperforms existing models on three standard nested NER datasets. |
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition (2023.acl-short)
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| Challenge: | Named entity recognition (NER) is the task to detect and classify entity spans in text. |
| Approach: | They propose to use Convolutional Neural Network to model spatial relations in NER . they use three commonly used nested NER datasets to compare their results . |
| Outcome: | The proposed model outperforms several proposed methods with the same pre-trained encoders in three nested NER datasets. |
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. |
| Approach: | They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them . |
| Outcome: | The proposed method outperforms state-of-the-art models on nested NER datasets. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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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. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |