# Recent Trends in Named Entity Recognition (NER)

arXiv: https://arxiv.org/abs/2101.11420 · HTML: https://mlsanity.com/paper/2101.11420/

## 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 (most cited first, 30 of 34)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling](https://mlsanity.com/paper/1312.3005/) (2013)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Harnessing Deep Neural Networks with Logic Rules](https://mlsanity.com/paper/1603.06318/) (2016)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017)
- [Mimicking Word Embeddings using Subword RNNs](https://mlsanity.com/paper/1707.06961/) (2017)
- [Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation](https://mlsanity.com/paper/1508.02096/) (2015)
- [Multilingual Language Processing From Bytes](https://mlsanity.com/paper/1512.00103/) (2015)
- [Semi-supervised Multitask Learning for Sequence Labeling](https://mlsanity.com/paper/1704.07156/) (2017)
- [Medical Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1704.06841/) (2017)
- [Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning](https://mlsanity.com/paper/1705.11168/) (2017)
- [eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys](https://mlsanity.com/paper/1702.08568/) (2017)
- [Lexicon Infused Phrase Embeddings for Named Entity Resolution](https://mlsanity.com/paper/1404.5367/) (2014)
- [High-risk learning: acquiring new word vectors from tiny data](https://mlsanity.com/paper/1707.06556/) (2017)
- [Making Sense of Word Embeddings](https://mlsanity.com/paper/1708.03390/) (2017)
- [Boosting Named Entity Recognition with Neural Character Embeddings](https://mlsanity.com/paper/1505.05008/) (2015)
- [Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs](https://mlsanity.com/paper/1508.00657/) (2015)

## Built on by (most cited first, 9 of 9)

- [OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2505.04416/) (2025)
- [Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations](https://mlsanity.com/paper/2502.11269/) (2025)
- [NERDA-Con: Extending NER models for Continual Learning -- Integrating Distinct Tasks and Updating Distribution Shifts](https://mlsanity.com/paper/2206.14607/) (2022)
- [Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification](https://mlsanity.com/paper/2206.02206/) (2022)
- [#maskUp: Selective Attribute Encryption for Sensitive Vocalization for English language on Social Media Platforms](https://mlsanity.com/paper/2211.08653/) (2022)
- [Adversarial Adaptation for French Named Entity Recognition](https://mlsanity.com/paper/2301.05220/) (2023)
- [Entity Extraction from High-Level Corruption Schemes via Large Language Models](https://mlsanity.com/paper/2409.13704/) (2024)
- [Leveraging Weighted Syntactic and Semantic Context Assessment Summary (wSSAS) Towards Text Categorization Using LLMs](https://mlsanity.com/paper/2604.12049/) (2026)
- [Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)](https://mlsanity.com/paper/2604.15547/) (2026)

## 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.