# TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

arXiv: https://arxiv.org/abs/2004.07493 · HTML: https://mlsanity.com/paper/2004.07493/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

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

- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models](https://mlsanity.com/paper/2109.05620/) (2021)
- [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition](https://mlsanity.com/paper/2108.11857/) (2021)
- [On Evaluating Explanation Utility for Human-AI Decision Making in NLP](https://mlsanity.com/paper/2407.03545/) (2024)
- [XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models](https://mlsanity.com/paper/2210.16978/) (2022)
- [ER-Test: Evaluating Explanation Regularization Methods for Language Models](https://mlsanity.com/paper/2205.12542/) (2022)
- [A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries](https://mlsanity.com/paper/2303.06944/) (2023)
- [Larger-Context Tagging: When and Why Does It Work?](https://mlsanity.com/paper/2104.04434/) (2021)
- [REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization](https://mlsanity.com/paper/2310.14418/) (2023)
- [A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection](https://mlsanity.com/paper/2412.11851/) (2024)
- [Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2204.05518/) (2022)
- [DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.08104/) (2022)
- [EROS: Entity-Driven Controlled Policy Document Summarization](https://mlsanity.com/paper/2403.00141/) (2024)
- [AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction](https://mlsanity.com/paper/2109.04726/) (2021)
- [Few-shot Named Entity Recognition with Cloze Questions](https://mlsanity.com/paper/2111.12421/) (2021)
- [Distilling Task-specific Logical Rules from Large Pre-trained Models](https://mlsanity.com/paper/2210.02768/) (2022)
- [Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension](https://mlsanity.com/paper/2308.06454/) (2023)
- [Do Language Models Learn about Legal Entity Types during Pretraining?](https://mlsanity.com/paper/2310.13092/) (2023)
- [Teach me how to Label: Labeling Functions from Natural Language with Text-to-text Transformers](https://mlsanity.com/paper/2101.07138/) (2021)
- [Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger](https://mlsanity.com/paper/2109.07118/) (2021)
- [BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic](https://mlsanity.com/paper/2201.09523/) (2022)
- [Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop](https://mlsanity.com/paper/2301.09908/) (2023)
- [A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods](https://mlsanity.com/paper/2310.19055/) (2023)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)
- [How much reliable is ChatGPT's prediction on Information Extraction under Input Perturbations?](https://mlsanity.com/paper/2404.05088/) (2024)

## Abstract (arXiv)

Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models. An entity trigger is defined as a group of words in a sentence that helps to explain why humans would recognize an entity in the sentence. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Our framework is significantly more cost-effective than the traditional neural NER frameworks. Experiments show that using only 20% of the trigger-annotated sentences results in a comparable performance as using 70% of conventional annotated sentences.