mlsanityData to 7 Oct 2026

Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition

arXiv 2406.12779 ↗First posted 18 Jun 2024cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Nested Named Entity Recognition (NNER) focuses on addressing overlapped entity recognition. Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER. Data augmentation is an effective approach to address the insufficient annotated corpus. However, there is a significant lack of exploration in data augmentation methods for NNER. Due to the presence of nested entities in NNER, existing data augmentation methods cannot be directly applied to NNER tasks. Therefore, in this work, we focus on data augmentation for NNER and resort to more expressive structures, Composited-Nested-Label Classification (CNLC) in which constituents are combined by nested-word and nested-label, to model nested entities. The dataset is augmented using the Composited-Nested-Learning (CNL). In addition, we propose the Confidence Filtering Mechanism (CFM) for a more efficient selection of generated data. Experimental results demonstrate that this approach results in improvements in ACE2004 and ACE2005 and alleviates the impact of sample imbalance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

17 of 23 references.

References · 17

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 994
  4. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  5. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  6. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  7. 2020Named Entity Recognition as Dependency Parsingcited by 150
  8. 2021A Unified Generative Framework for Various NER Subtaskscited by 141
  9. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 75
  10. 2021MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERcited by 42
  11. 2021A Sequence-to-Set Network for Nested Named Entity Recognitioncited by 40
  12. 2023Understanding In-Context Learning from Repetitionscited by 27
5 more
  1. 2020Nested Named Entity Recognition with Partially-Observed TreeCRFscited by 23
  2. 2021Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networkscited by 21
  3. 2021Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognitioncited by 18
  4. 2023PromptNER: Prompt Locating and Typing for Named Entity Recognitioncited by 17
  5. 2023ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERcited by 9

Built on by

Not cited yet.