Automatic Noisy Label Correction for Fine-Grained Entity Typing

TL;DR

This paper introduces a novel method for automatically correcting noisy labels in fine-grained entity typing tasks without relying on external resources, improving accuracy on benchmark datasets.

Contribution

It proposes a new approach that identifies and relabels noisy data in FET using model logits, eliminating the need for auxiliary resources.

Findings

Effective noise correction demonstrated on benchmark datasets

Improved FET performance without external resources

Method outperforms previous noise identification techniques

Abstract

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weakly-supervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance of the FET task. Although previous studies have made great success in automatically identifying the noisy labels in FET, they usually rely on some auxiliary resources which may be unavailable in real-world applications (e.g. pre-defined hierarchical type structures, human-annotated subsets). In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel candidate noisy labels by training a robust model over the remaining clean labels. Experiments on two popular benchmarks prove the effectiveness of our method. Our source code can be obtained from https://github.com/CCIIPLab/DenoiseFET.