DeepAI
Log In Sign Up

Noisy-Labeled NER with Confidence Estimation

04/09/2021
by   Kun Liu, et al.
0

Recent studies in deep learning have shown significant progress in named entity recognition (NER). Most existing works assume clean data annotation, yet a fundamental challenge in real-world scenarios is the large amount of noise from a variety of sources (e.g., pseudo, weak, or distant annotations). This work studies NER under a noisy labeled setting with calibrated confidence estimation. Based on empirical observations of different training dynamics of noisy and clean labels, we propose strategies for estimating confidence scores based on local and global independence assumptions. We partially marginalize out labels of low confidence with a CRF model. We further propose a calibration method for confidence scores based on the structure of entity labels. We integrate our approach into a self-training framework for boosting performance. Experiments in general noisy settings with four languages and distantly labeled settings demonstrate the effectiveness of our method. Our code can be found at https://github.com/liukun95/Noisy-NER-Confidence-Estimation

READ FULL TEXT

page 1

page 2

page 3

page 4

06/28/2020

BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision

We study the open-domain named entity recognition (NER) problem under di...
09/10/2021

Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

We study the problem of training named entity recognition (NER) models u...
11/23/2021

CL-NERIL: A Cross-Lingual Model for NER in Indian Languages

Developing Named Entity Recognition (NER) systems for Indian languages h...
06/17/2021

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Denoising is the essential step for distant supervision based named enti...
04/26/2022

Boundary Smoothing for Named Entity Recognition

Neural named entity recognition (NER) models may easily encounter the ov...
08/19/2021

QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction

We study the problem of query attribute value extraction, which aims to ...
10/14/2019

Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels

In low-resource settings, the performance of supervised labeling models ...