# Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

arXiv: https://arxiv.org/abs/2010.15466 · HTML: https://mlsanity.com/paper/2010.15466/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization](https://mlsanity.com/paper/2205.06009/) (2022)
- [Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge](https://mlsanity.com/paper/2105.02520/) (2021)
- [Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling](https://mlsanity.com/paper/2110.02001/) (2021)
- [ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders](https://mlsanity.com/paper/2105.01279/) (2021)
- [Improving Image Captioning via Predicting Structured Concepts](https://mlsanity.com/paper/2311.08223/) (2023)
- [Causality-aware Concept Extraction based on Knowledge-guided Prompting](https://mlsanity.com/paper/2305.01876/) (2023)
- [Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach](https://mlsanity.com/paper/2111.09415/) (2021)
- [Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting](https://mlsanity.com/paper/2204.11406/) (2022)
- [AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction](https://mlsanity.com/paper/2109.04726/) (2021)
- [Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension](https://mlsanity.com/paper/2308.06454/) (2023)
- [An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition](https://mlsanity.com/paper/2109.01293/) (2021)
- [NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2205.05832/) (2022)
- [Hero-Gang Neural Model For Named Entity Recognition](https://mlsanity.com/paper/2205.07177/) (2022)
- [Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition](https://mlsanity.com/paper/2210.10240/) (2022)
- [End-to-End Entity Detection with Proposer and Regressor](https://mlsanity.com/paper/2210.10260/) (2022)

## Abstract (arXiv)

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets.