Using Domain Knowledge for Low Resource Named Entity Recognition

TL;DR

This paper introduces a method that leverages domain knowledge, such as dictionaries and labeled data, to improve low-resource named entity recognition without extensive data adjustments across domains.

Contribution

It proposes a novel approach using domain dictionaries and labeled data to enhance NER performance in resource-scarce areas, avoiding large-scale domain-specific data adjustments.

Findings

Significant F1 score improvement on scientific and technological equipment dataset.

Effective use of domain dictionaries to reinforce word embeddings.

Outperforms several baseline methods in low-resource NER tasks.

Abstract

In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods.