# Dependency-Guided LSTM-CRF for Named Entity Recognition

arXiv: https://arxiv.org/abs/1909.10148 · HTML: https://mlsanity.com/paper/1909.10148/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 28 of 28)

- [LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model](https://mlsanity.com/paper/2304.06248/) (2023)
- [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)
- [Uncovering Main Causalities for Long-tailed Information Extraction](https://mlsanity.com/paper/2109.05213/) (2021)
- [OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction](https://mlsanity.com/paper/2209.02693/) (2022)
- [Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition](https://mlsanity.com/paper/2208.03054/) (2022)
- [Activated Gradients for Deep Neural Networks](https://mlsanity.com/paper/2107.04228/) (2021)
- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Headed-Span-Based Projective Dependency Parsing](https://mlsanity.com/paper/2108.04750/) (2021)
- [Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition](https://mlsanity.com/paper/2204.05544/) (2022)
- [To be Closer: Learning to Link up Aspects with Opinions](https://mlsanity.com/paper/2109.08382/) (2021)
- [Span Classification with Structured Information for Disfluency Detection in Spoken Utterances](https://mlsanity.com/paper/2203.16028/) (2022)
- [Improving Topic Segmentation by Injecting Discourse Dependencies](https://mlsanity.com/paper/2209.08626/) (2022)
- [VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition](https://mlsanity.com/paper/2212.07219/) (2022)
- [Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach](https://mlsanity.com/paper/2111.09415/) (2021)
- [Korean Named Entity Recognition Based on Language-Specific Features](https://mlsanity.com/paper/2305.06330/) (2023)
- [Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition](https://mlsanity.com/paper/2207.03300/) (2022)
- [Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media](https://mlsanity.com/paper/2210.14163/) (2022)
- [Exploiting Global Contextual Information for Document-level Named Entity Recognition](https://mlsanity.com/paper/2106.00887/) (2021)
- [Nested and Balanced Entity Recognition using Multi-Task Learning](https://mlsanity.com/paper/2106.06216/) (2021)
- [Powering Effective Climate Communication with a Climate Knowledge Base](https://mlsanity.com/paper/2107.11351/) (2021)
- [Extracting Impact Model Narratives from Social Services' Text](https://mlsanity.com/paper/2204.09557/) (2022)
- [NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2205.05832/) (2022)
- [A Global Context Mechanism for Sequence Labeling](https://mlsanity.com/paper/2305.19928/) (2023)
- [Named entity recognition in resumes](https://mlsanity.com/paper/2306.13062/) (2023)
- [Cross-Lingual News Event Correlation for Stock Market Trend Prediction](https://mlsanity.com/paper/2410.00024/) (2024)
- [Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition](https://mlsanity.com/paper/2503.05373/) (2025)
- [CodeNER: Code Prompting for Named Entity Recognition](https://mlsanity.com/paper/2507.20423/) (2025)

## Abstract (arXiv)

Dependency tree structures capture long-distance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially infer the existence of certain named entities. In addition, the performance of a named entity recognizer could benefit from the long-distance dependencies between the words in dependency trees. In this work, we propose a simple yet effective dependency-guided LSTM-CRF model to encode the complete dependency trees and capture the above properties for the task of named entity recognition (NER). The data statistics show strong correlations between the entity types and dependency relations. We conduct extensive experiments on several standard datasets and demonstrate the effectiveness of the proposed model in improving NER and achieving state-of-the-art performance. Our analysis reveals that the significant improvements mainly result from the dependency relations and long-distance interactions provided by dependency trees.