Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach

• URL: http://arxiv.org/abs/2402.14948v2
• Date: Mon, 26 Feb 2024 14:59:58 GMT
• Title: Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
• Authors: Yuepei Li, Kang Zhou, Qiao Qiao, Qing Wang and Qi Li
• Abstract summary: We critically assess the efficacy of current DS-NER methodologies using a real-world benchmark dataset named QTL. To tackle the prevalent issue of label noise, we introduce a simple yet effective approach, Curriculum-based Positive-Unlabeled Learning CuPUL. Our empirical results highlight the capability of CuPUL to significantly reduce the impact of noisy labels and outperform existing methods.
• Score: 15.87963432758696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper delves into Named Entity Recognition (NER) under the framework of Distant Supervision (DS-NER), where the main challenge lies in the compromised quality of labels due to inherent errors such as false positives, false negatives, and positive type errors. We critically assess the efficacy of current DS-NER methodologies using a real-world benchmark dataset named QTL, revealing that their performance often does not meet expectations. To tackle the prevalent issue of label noise, we introduce a simple yet effective approach, Curriculum-based Positive-Unlabeled Learning CuPUL, which strategically starts on "easy" and cleaner samples during the training process to enhance model resilience to noisy samples. Our empirical results highlight the capability of CuPUL to significantly reduce the impact of noisy labels and outperform existing methods. QTL dataset and our code is available on GitHub.

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