Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023)

Abstract: Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation techniques either manipulate the words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the original text, which impedes the use of augmentation techniques on nested and discontinuous NER tasks. In this work, we propose a novel Entity-to-Text based data augmentation technique named EnTDA to add, delete, replace or swap entities in the entity list of the original texts, and adopt these augmented entity lists to generate semantically coherent and entity preserving texts for various NER tasks. Furthermore, we introduce a diversity beam search to increase the diversity during the text generation process. Experiments on thirteen NER datasets across three tasks (flat, nested, and discontinuous NER tasks) and two settings (full data and low resource settings) show that EnTDA could bring more performance improvements compared to the baseline augmentation techniques.

## introduction recent neural networks show decent performance when a large amount of training data is available. however, these manually labeled data are laborintensive to obtain. data augmentation techniques @xcite expand the training set by generating synthetic data to improve the generalization and scalability of deep neural networks, and are widely used in nlp @xcite . one successful attempt for data augmentation in nlp is manipulating a few words in the original text, such as word swapping @xcite and random deletion @xcite aug. text ## general ner task formulation considering that entda has sufficient augmentation ability on flat, nested and discontinuous ner, we first formulate the general ner task framework as follows. given an input text @xmath0, x 2 , ..., x n ] of length n and the entity type set t , the output is an entity list @xmath1, e 2 , ..., e m , ..., e l ] of l entities, where e @xmath2, d m1 , ..., s mj , d mj , t m ]. the s, d are the start and end indexes of a space in the text x. the j indicates that the entity consists of j spans. the t m is an entity type in the entity type set t . for example, the discontinuous entity stomach pain in the text: "the cancer patient has constant stomach discomfort and pain" will be represented as e @xmath3, 5, 8, 8, disorder]. ## proposed method the proposed entity-to-text based data augmentation approach entda consists of three modules: entity list augmentation, entity-to-text generation, and augmented text exploitation. now we give the details of the three modules. ## experiments and analyses we conduct extensive experiments on thirteen ner datasets across three tasks (flat, nested, and discontinuous ner) and two settings (full data and low resource ner) to show the effectiveness of entda on ner, and give a detailed analysis. ## case study we show eight approaches to obtain augmented data for the ai domain in ## conclusions and future work in this paper, we propose an entity-to-text based data augmentation approach entda for ner tasks. compared with traditional rule-based augmentation methods that break semantic coherence, or use text-to-text based augmentation methods that cannot be used on nested and discontinuous ner tasks, our method can generate semantically coherent texts for all ner tasks, and use the diversity beam search to improve the diversity of augmented texts. experiments on thirteen public real-world datasets, and coherence and diversity analysis show the effectiveness of entda. moreover, we can also apply the method of data augmentation to low-resource relation extraction @xcite @xcite , natural language inference @xcite , semantic parsing @xcite , and other nlp application tasks, thus realizing knowledge enhancement based on data augmentation approach.