# Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing

arXiv: https://arxiv.org/abs/2203.04665 · HTML: https://mlsanity.com/paper/2203.04665/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 70 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Fast and Accurate Neural CRF Constituency Parsing](https://mlsanity.com/paper/2008.03736/) (2020)
- [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://mlsanity.com/paper/2005.00975/) (2020)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [Torch-Struct: Deep Structured Prediction Library](https://mlsanity.com/paper/2002.00876/) (2020)
- [A Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [Nested Named Entity Recognition via Second-best Sequence Learning and Decoding](https://mlsanity.com/paper/1909.02250/) (2019)
- [Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks](https://mlsanity.com/paper/1906.03783/) (2019)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Neural Segmental Hypergraphs for Overlapping Mention Recognition](https://mlsanity.com/paper/1810.01817/) (2018)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [Nested Named Entity Recognition with Partially-Observed TreeCRFs](https://mlsanity.com/paper/2012.08478/) (2020)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [The Return of Lexical Dependencies: Neural Lexicalized PCFGs](https://mlsanity.com/paper/2007.15135/) (2020)
- [Neural Bi-Lexicalized PCFG Induction](https://mlsanity.com/paper/2105.15021/) (2021)
- [Merge and Label: A novel neural network architecture for nested NER](https://mlsanity.com/paper/1907.00464/) (2019)
- [A Neural Transition-based Model for Nested Mention Recognition](https://mlsanity.com/paper/1810.01808/) (2018)
- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Multitask Pointer Network for Multi-Representational Parsing](https://mlsanity.com/paper/2009.09730/) (2020)

## Built on by (most cited first, 14 of 14)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023)
- [PromptNER: Prompt Locating and Typing for Named Entity Recognition](https://mlsanity.com/paper/2305.17104/) (2023)
- [A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends](https://mlsanity.com/paper/2302.03512/) (2023)
- [RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction](https://mlsanity.com/paper/2304.14770/) (2023)
- [DiffusionNER: Boundary Diffusion for Named Entity Recognition](https://mlsanity.com/paper/2305.13298/) (2023)
- [A dynamic programming algorithm for span-based nested named-entity recognition in O(n^2)](https://mlsanity.com/paper/2210.04738/) (2022)
- [Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition](https://mlsanity.com/paper/2305.07266/) (2023)
- [To be Continuous, or to be Discrete, Those are Bits of Questions](https://mlsanity.com/paper/2406.07812/) (2024)
- [Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field](https://mlsanity.com/paper/2212.08929/) (2022)
- [DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit](https://mlsanity.com/paper/2310.09501/) (2023)
- [Filtered Semi-Markov CRF](https://mlsanity.com/paper/2311.18028/) (2023)
- [Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing](https://mlsanity.com/paper/2407.04801/) (2024)
- [Nested Named Entity Recognition as Single-Pass Sequence Labeling](https://mlsanity.com/paper/2505.16855/) (2025)

## Abstract (arXiv)

Nested named entity recognition (NER) has been receiving increasing attention. Recently, (Fu et al, 2021) adapt a span-based constituency parser to tackle nested NER. They treat nested entities as partially-observed constituency trees and propose the masked inside algorithm for partial marginalization. However, their method cannot leverage entity heads, which have been shown useful in entity mention detection and entity typing. In this work, we resort to more expressive structures, lexicalized constituency trees in which constituents are annotated by headwords, to model nested entities. We leverage the Eisner-Satta algorithm to perform partial marginalization and inference efficiently. In addition, we propose to use (1) a two-stage strategy (2) a head regularization loss and (3) a head-aware labeling loss in order to enhance the performance. We make a thorough ablation study to investigate the functionality of each component. Experimentally, our method achieves the state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive performance on GENIA, and meanwhile has a fast inference speed.