Weakly Supervised Named Entity Tagging with Learnable Logical Rules

TL;DR

This paper introduces TALLOR, a fully automated weakly supervised method for named entity tagging that bootstraps logical rules to improve boundary detection and pseudo label quality, outperforming existing methods.

Contribution

TALLOR automatically learns compound logical rules for entity boundary detection, reducing reliance on manual annotations and enhancing weak supervision for named entity tagging.

Findings

Outperforms other weakly supervised methods on three datasets.

Rivals state-of-the-art distantly supervised taggers with minimal rules.

Learned rules can explain predicted entities.

Abstract

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.