Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning

• URL: http://arxiv.org/abs/2204.09589v1
• Date: Thu, 3 Mar 2022 17:55:35 GMT
• Title: Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning
• Authors: Kang Zhou, Yuepei Li, Qi Li
• Abstract summary: We formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning. We propose a theoretically and practically novel MPU (Conf-MPU) approach to handle the incomplete annotations.
• Score: 9.674267150358789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the named entity recognition (NER) problem under distant supervision. Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data usually suffer from a high false negative rate. To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach. To handle the incomplete annotations, Conf-MPU consists of two steps. First, a confidence score is estimated for each token of being an entity token. Then, the proposed Conf-MPU risk estimation is applied to train a multi-class classifier for the NER task. Thorough experiments on two benchmark datasets labeled by various external knowledge demonstrate the superiority of the proposed Conf-MPU over existing DS-NER methods.

Related papers

• SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities [10.193908215351497]
  We propose SCANNER, a model capable of effectively handling all three NER variants. SCANNER is a two-stage structure; we extract entity candidates in the first stage and use it as a query to get knowledge. To tackle the challenges arising from noisy annotations in NER datasets, we introduce a novel self-distillation method.
  arXiv  Detail & Related papers  (2024-04-02T13:05:41Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach [50.12455129619845]
  Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types. We propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.
  arXiv  Detail & Related papers  (2023-09-20T03:15:05Z)
• Robust Representation Learning for Unreliable Partial Label Learning [86.909511808373]
  Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels. We propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively.
  arXiv  Detail & Related papers  (2023-08-31T13:37:28Z)
• HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation [40.52832708232682]
  Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. We propose a Hierarchical Reliability Propagation framework, which consists of two reliability-aware stages: Snippet-level Discrimination Learning and Instance-level Completeness Learning. Our HR-Pro achieves state-of-the-art performance on multiple challenging benchmarks, including an impressive average mAP of 60.3% on THUMOS14.
  arXiv  Detail & Related papers  (2023-08-24T07:19:11Z)
• E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition [69.87816981427858]
  Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. We propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies.
  arXiv  Detail & Related papers  (2023-05-29T02:36:16Z)
• A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition [74.79785063365289]
  Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets. We propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
  arXiv  Detail & Related papers  (2023-05-21T15:31:23Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.