# Efficient Dependency-Guided Named Entity Recognition

arXiv: https://arxiv.org/abs/1810.08436 · HTML: https://mlsanity.com/paper/1810.08436/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Better Feature Integration for Named Entity Recognition](https://mlsanity.com/paper/2104.05316/) (2021)
- [GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models](https://mlsanity.com/paper/2509.07925/) (2025)
- [TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning](https://mlsanity.com/paper/2412.02570/) (2024)
- [Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements](https://mlsanity.com/paper/2505.13837/) (2025)

## Abstract (arXiv)

Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the other hand, also convey crucial semantic-level information. It has been shown previously that such information can be used to improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012). In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER. Specifically, unlike existing approaches which only exploit dependency information for designing local features, we show that certain global structured information of the dependency trees can be exploited when building NER models where such information can provide guided learning and inference. Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time.