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

arXiv:2402.14948v1 Announce Type: cross
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 …

abstract arxiv benchmark challenge cs.cl cs.lg errors false false positives framework labels lies ner paper positive quality recognition simple supervision type