Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets

Su Ah Lee, Seokjin Oh, Woohwan Jung

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Keywords: Named Entity Recognition, Fine-grained NER, Low-resource scenario

Abstract:

Named Entity Recognition (NER) frequently suffers from the problem of insufficient labeled data, particularly in fine-grained NER scenarios.
Although $K$-shot learning techniques can be applied, their performance tends to saturate when the number of annotations exceeds several tens of labels.
To overcome this problem, we utilize existing coarse-grained datasets that offer a large number of annotations.
A straightforward approach to address this problem is pre-finetuning, which employs coarse-grained data for representation learning.
However, it cannot directly utilize the relationships between fine-grained and coarse-grained entities, although a fine-grained entity type is likely to be a subcategory of a coarse-grained entity type.
We propose a fine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leverage the hierarchical structure explicitly.
In addition, we present an inconsistency filtering method to eliminate coarse-grained entities that are inconsistent with fine-grained entity types to avoid performance degradation.
Our experimental results show that our method outperforms both $K$-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations.

Submission Number: 1349