Data set entity recognition based on distant supervision

Abstract

Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains

“…Entity Replacement (ER) [7] A variant of MR, ER replaces entities with alternative entities from sources other than the original training set.…”

“…To both adhere to established research and examine the cost-effectiveness of data augmentation across different model types, we opted to utilize both architectures. For data augmentation, we focused on methods that are well regarded in the literature and have demonstrated improvements in the performance of the NER model [4,5,7,27], while also being relatively simple to implement. Hence, we chose Mention Replacement (MR) for augmenting tokens tagged with specific entity types, excluding those labeled "O," and Contextual Word Replacement (CWR) for tokens specifically tagged as "O."…”

“…In the work of Liu et al [7], a comprehensive examination of text augmentation techniques, namely ER and EM (Section 2), was conducted on a dataset encompassing long-tail entities within academic papers. The authors evaluated the impact of varying data augmentation scales: 20%, 50%, and 100% of the original dataset.…”

“…These domains have been extensively studied, and numerous datasets are readily available for research (e.g., [2,3]). In contrast, entities in specialized application domains, also called low-resource domains [4,5], like medical, legal, and financial sectors, are termed as long-tail entities [6,7]. These domains are less commonly found in typical online text corpora.…”

“…This approach automatically generates new data samples by applying transformations to existing data [13][14][15]. Originally popularized in the field of computer vision, data augmentation is gaining traction in Natural Language Processing (NLP) tasks, including NER [4,5,7,16,17]. For example, a sentence like I have a cat named Serena could be transformed into You have a dog named Beethoven or I own a cat called Serena, thereby enriching the training set.…”