GapDNER: A Gap-Aware Grid Tagging Model for Discontinuous Named Entity Recognition

• URL: http://arxiv.org/abs/2510.10927v1
• Date: Mon, 13 Oct 2025 02:40:14 GMT
• Title: GapDNER: A Gap-Aware Grid Tagging Model for Discontinuous Named Entity Recognition
• Authors: Yawen Yang, Fukun Ma, Shiao Meng, Aiwei Liu, Lijie Wen,
• Abstract summary: In biomedical fields, one named entity may consist of a series of non-adjacent tokens and overlap with other entities.<n>Previous methods recognize discontinuous entities by connecting entity fragments or internal tokens.<n>We propose an effective Gap-aware grid tagging model for Discontinuous Named Entity Recognition, named GapDNER.
• Score: 22.669221793494163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In biomedical fields, one named entity may consist of a series of non-adjacent tokens and overlap with other entities. Previous methods recognize discontinuous entities by connecting entity fragments or internal tokens, which face challenges of error propagation and decoding ambiguity due to the wide variety of span or word combinations. To address these issues, we deeply explore discontinuous entity structures and propose an effective Gap-aware grid tagging model for Discontinuous Named Entity Recognition, named GapDNER. Our GapDNER innovatively applies representation learning on the context gaps between entity fragments to resolve decoding ambiguity and enhance discontinuous NER performance. Specifically, we treat the context gap as an additional type of span and convert span classification into a token-pair grid tagging task. Subsequently, we design two interactive components to comprehensively model token-pair grid features from both intra- and inter-span perspectives. The intra-span regularity extraction module employs the biaffine mechanism along with linear attention to capture the internal regularity of each span, while the inter-span relation enhancement module utilizes criss-cross attention to obtain semantic relations among different spans. At the inference stage of entity decoding, we assign a directed edge to each entity fragment and context gap, then use the BFS algorithm to search for all valid paths from the head to tail of grids with entity tags. Experimental results on three datasets demonstrate that our GapDNER achieves new state-of-the-art performance on discontinuous NER and exhibits remarkable advantages in recognizing complex entity structures.

Related papers

• TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition [13.490098281895877]
  TriG-NER is a novel Triplet-Grid Framework for learning robust token-level representations for discontinuous entity extraction.<n>Our framework applies triplet loss at the token level, where similarity is defined by word pairs existing within the same entity.<n>We evaluate TriG-NER on three benchmark DNER datasets and demonstrate significant improvements over existing grid-based architectures.
  arXiv  Detail & Related papers  (2024-11-04T06:26:09Z)
• A Generative Approach for Wikipedia-Scale Visual Entity Recognition [56.55633052479446]
  We address the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. We introduce a novel Generative Entity Recognition framework, which learns to auto-regressively decode a semantic and discriminative code'' identifying the target entity.
  arXiv  Detail & Related papers  (2024-03-04T13:47:30Z)
• A Boundary Offset Prediction Network for Named Entity Recognition [9.885278527023532]
  Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. We propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. Our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets.
  arXiv  Detail & Related papers  (2023-10-23T05:04:07Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition [35.44123819012004]
  Named entity recognition (NER) is an important research problem in natural language processing. This paper proposes a multi-task Transformer, which incorporates an entity boundary detection task into the named entity recognition task.
  arXiv  Detail & Related papers  (2023-03-20T05:11:22Z)
• DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [78.30720731968135]
  Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. We propose DecoupleNet that alleviates source domain overfitting and enables the final model to focus more on the segmentation task. We also put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels.
  arXiv  Detail & Related papers  (2022-07-20T15:47:34Z)
• A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation [52.911307452212256]
  We develop a unified architecture of semantic segmentation and hierarchical GANs. A unique advantage of our framework is that on forward pass the semantic segmentation network conditions the generative model. We evaluate our method on two challenging facial expression translation benchmarks, AffectNet and RaFD, and a semantic segmentation benchmark, CelebAMask-HQ.
  arXiv  Detail & Related papers  (2021-12-08T22:06:31Z)
• A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition [24.673897695262024]
  We propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. Experimental results on multiple benchmark datasets show that our model is highly competitive for overlapped and discontinuous NER.
  arXiv  Detail & Related papers  (2021-06-28T02:37:20Z)
• A Sequence-to-Set Network for Nested Named Entity Recognition [38.05786148160635]
  We propose a novel sequence-to-set neural network for nested NER. We use a non-autoregressive decoder to predict the final set of entities in one pass. Experimental results show that our proposed model achieves state-of-the-art on three nested NER corpora.
  arXiv  Detail & Related papers  (2021-05-19T03:10:04Z)
• CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection [91.91911418421086]
  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. We present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images.
  arXiv  Detail & Related papers  (2020-11-10T04:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.