# Weakly Supervised Named Entity Tagging with Learnable Logical Rules

arXiv: https://arxiv.org/abs/2107.02282 · HTML: https://mlsanity.com/paper/2107.02282/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018)
- [Learning the Structure of Generative Models without Labeled Data](https://mlsanity.com/paper/1703.00854/) (2017)
- [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data](https://mlsanity.com/paper/1704.06360/) (2017)
- [Empower Entity Set Expansion via Language Model Probing](https://mlsanity.com/paper/2004.13897/) (2020)
- [Generalizing Natural Language Analysis through Span-relation Representations](https://mlsanity.com/paper/1911.03822/) (2019)
- [Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets](https://mlsanity.com/paper/1709.05094/) (2017)

## Built on by (most cited first, 21 of 21)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Multi-task Active Learning for Pre-trained Transformer-based Models](https://mlsanity.com/paper/2208.05379/) (2022)
- [PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning](https://mlsanity.com/paper/2203.09735/) (2022)
- [Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming](https://mlsanity.com/paper/2203.01382/) (2022)
- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [Simple Questions Generate Named Entity Recognition Datasets](https://mlsanity.com/paper/2112.08808/) (2021)
- [SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.05632/) (2022)
- [Natural Language Processing for Cognitive Analysis of Emotions](https://mlsanity.com/paper/2210.05296/) (2022)
- [ScriptoriumWS: A Code Generation Assistant for Weak Supervision](https://mlsanity.com/paper/2502.12366/) (2025)
- [RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules](https://mlsanity.com/paper/2403.02932/) (2024)
- [SPOT: Knowledge-Enhanced Language Representations for Information Extraction](https://mlsanity.com/paper/2208.09625/) (2022)
- [Distilling Task-specific Logical Rules from Large Pre-trained Models](https://mlsanity.com/paper/2210.02768/) (2022)
- [Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations](https://mlsanity.com/paper/2210.07586/) (2022)
- [Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension](https://mlsanity.com/paper/2308.06454/) (2023)
- [ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models](https://mlsanity.com/paper/2403.11103/) (2024)
- [Automating Weak Label Generation for Data Programming with Clinicians in the Loop](https://mlsanity.com/paper/2407.07982/) (2024)
- [Structured Exploration and Exploitation of Label Functions for Automated Data Annotation](https://mlsanity.com/paper/2604.08578/) (2026)
- [AutoWS: Automated Weak Supervision Framework for Text Classification](https://mlsanity.com/paper/2302.03297/) (2023)
- [Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation](https://mlsanity.com/paper/2407.02719/) (2024)
- [An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots](https://mlsanity.com/paper/2410.07094/) (2024)

## Abstract (arXiv)

We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguation entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.