mlsanityData to 14 Oct 2026

Recent Trends in Named Entity Recognition (NER)

arXiv 2101.11420 ↗First posted 25 Jan 2021cs.CLcited by 9

Not on the map. Why?

Abstract (arXiv)

The availability of large amounts of computer-readable textual data and hardware that can process the data has shifted the focus of knowledge projects towards deep learning architecture. Natural Language Processing, particularly the task of Named Entity Recognition is no exception. The bulk of the learning methods that have produced state-of-the-art results have changed the deep learning model, the training method used, the training data itself or the encoding of the output of the NER system. In this paper, we review significant learning methods that have been employed for NER in the recent past and how they came about from the linear learning methods of the past. We also cover the progress of related tasks that are upstream or downstream to NER, e.g., sequence tagging, entity linking, etc., wherever the processes in question have also improved NER results.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 34 of its 117 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

34 of 117 references.

References · 34

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22 more
  1. 2014A Convolutional Neural Network for Modelling Sentencescited by 163
  2. 2016Harnessing Deep Neural Networks with Logic Rulescited by 135
  3. 2015Character-Aware Neural Language Modelscited by 132
  4. 2017Semi-supervised sequence tagging with bidirectional language modelscited by 86
  5. 2015Transition-Based Dependency Parsing with Stack Long Short-Term Memorycited by 45
  6. 2017Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networkscited by 30
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 2; 2026: 2.

9 papers.

  1. 2025OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Modelscited by 10
  2. 2025Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitationscited by 6
  3. 2022NERDA-Con: Extending NER models for Continual Learning -- Integrating Distinct Tasks and Updating Distribution Shiftscited by 2
  4. 2022Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classificationcited by 0
  5. 2022#maskUp: Selective Attribute Encryption for Sensitive Vocalization for English language on Social Media Platformscited by 0
  6. 2023Adversarial Adaptation for French Named Entity Recognitioncited by 0
  7. 2024Entity Extraction from High-Level Corruption Schemes via Large Language Modelscited by 0
  8. 2026Leveraging Weighted Syntactic and Semantic Context Assessment Summary (wSSAS) Towards Text Categorization Using LLMscited by 0
  9. 2026Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)cited by 0