# Learning Named Entity Tagger using Domain-Specific Dictionary

arXiv: https://arxiv.org/abs/1809.03599 · HTML: https://mlsanity.com/paper/1809.03599/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 30 of 65)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning](https://mlsanity.com/paper/2104.08350/) (2021)
- [Noisy-Labeled NER with Confidence Estimation](https://mlsanity.com/paper/2104.04318/) (2021)
- [Pushing the Limits of ChatGPT on NLP Tasks](https://mlsanity.com/paper/2306.09719/) (2023)
- [De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention](https://mlsanity.com/paper/2106.09233/) (2021)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [Weakly Supervised Named Entity Tagging with Learnable Logical Rules](https://mlsanity.com/paper/2107.02282/) (2021)
- [Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning](https://mlsanity.com/paper/2204.09589/) (2022)
- [EXTRACTOR: Extracting Attack Behavior from Threat Reports](https://mlsanity.com/paper/2104.08618/) (2021)
- [Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data](https://mlsanity.com/paper/2106.08977/) (2021)
- [skweak: Weak Supervision Made Easy for NLP](https://mlsanity.com/paper/2104.09683/) (2021)
- [Few-shot Named Entity Recognition with Self-describing Networks](https://mlsanity.com/paper/2203.12252/) (2022)
- [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021)
- [UCPhrase: Unsupervised Context-aware Quality Phrase Tagging](https://mlsanity.com/paper/2105.14078/) (2021)
- [Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble](https://mlsanity.com/paper/2212.06522/) (2022)
- [QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction](https://mlsanity.com/paper/2108.08468/) (2021)
- [GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2104.06230/) (2021)
- [Rethinking Negative Sampling for Handling Missing Entity Annotations](https://mlsanity.com/paper/2108.11607/) (2021)
- [Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning](https://mlsanity.com/paper/2110.04429/) (2021)
- [Domain-Specific NER via Retrieving Correlated Samples](https://mlsanity.com/paper/2208.12995/) (2022)
- [Mix of Experts Language Model for Named Entity Recognition](https://mlsanity.com/paper/2404.19192/) (2024)
- [Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition](https://mlsanity.com/paper/2108.11857/) (2021)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [BIOS: An Algorithmically Generated Biomedical Knowledge Graph](https://mlsanity.com/paper/2203.09975/) (2022)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model](https://mlsanity.com/paper/2106.09234/) (2021)
- [Self-Training with Differentiable Teacher](https://mlsanity.com/paper/2109.07049/) (2021)
- [A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries](https://mlsanity.com/paper/2303.06944/) (2023)

## Abstract (arXiv)

Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.