# Neural Architectures for Nested NER through Linearization

arXiv: https://arxiv.org/abs/1908.06926 · HTML: https://mlsanity.com/paper/1908.06926/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 30 of 56)

- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [Generative Knowledge Graph Construction: A Review](https://mlsanity.com/paper/2210.12714/) (2022)
- [A Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://mlsanity.com/paper/2105.11314/) (2021)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER](https://mlsanity.com/paper/2203.03903/) (2022)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [Boundary Smoothing for Named Entity Recognition](https://mlsanity.com/paper/2204.12031/) (2022)
- [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022)
- [Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition](https://mlsanity.com/paper/2110.07480/) (2021)
- [DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2203.00545/) (2022)
- [Findings of the Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2209.07841/) (2022)
- [Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder](https://mlsanity.com/paper/2109.04500/) (2021)
- [Understanding Model Robustness to User-generated Noisy Texts](https://mlsanity.com/paper/2110.07428/) (2021)
- [RuNNE-2022 Shared Task: Recognizing Nested Named Entities](https://mlsanity.com/paper/2205.11159/) (2022)
- [ToNER: Type-oriented Named Entity Recognition with Generative Language Model](https://mlsanity.com/paper/2404.09145/) (2024)
- [Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model](https://mlsanity.com/paper/2106.09234/) (2021)
- [An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition](https://mlsanity.com/paper/2208.04534/) (2022)
- [Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech](https://mlsanity.com/paper/2105.04719/) (2021)
- [CsFEVER and CTKFacts: Acquiring Czech data for fact verification](https://mlsanity.com/paper/2201.11115/) (2022)
- [DiffusionNER: Boundary Diffusion for Named Entity Recognition](https://mlsanity.com/paper/2305.13298/) (2023)
- [P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2405.04960/) (2024)
- [Deep Span Representations for Named Entity Recognition](https://mlsanity.com/paper/2210.04182/) (2022)
- [Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition](https://mlsanity.com/paper/2303.10870/) (2023)
- [A New Entity Extraction Method Based on Machine Reading Comprehension](https://mlsanity.com/paper/2108.06444/) (2021)

## Abstract (arXiv)

We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In our first proposed approach, the nested labels are modeled as multilabels corresponding to the Cartesian product of the nested labels in a standard LSTM-CRF architecture. In the second one, the nested NER is viewed as a sequence-to-sequence problem, in which the input sequence consists of the tokens and output sequence of the labels, using hard attention on the word whose label is being predicted. The proposed methods outperform the nested NER state of the art on four corpora: ACE-2004, ACE-2005, GENIA and Czech CNEC. We also enrich our architectures with the recently published contextual embeddings: ELMo, BERT and Flair, reaching further improvements for the four nested entity corpora. In addition, we report flat NER state-of-the-art results for CoNLL-2002 Dutch and Spanish and for CoNLL-2003 English.