Nested Named Entity Recognition: A Survey

Abstract

With the rapid development of text mining, many studies observe that text generally contains a variety of implicit information, and it is important to develop techniques for extracting such information. Named Entity Recognition (NER), the first step of information extraction, mainly identifies names of persons, locations, and organizations in text. Although existing neural-based NER approaches achieve great success in many language domains, most of them normally ignore the nested nature of named entities. Recently, diverse studies focus on the nested NER problem and yield state-of-the-art performance. This survey attempts to provide a comprehensive review on existing approaches for nested NER from the perspectives of the model architecture and the model property, which may help readers have a better understanding of the current research status and ideas. In this survey, we first introduce the background of nested NER, especially the differences between nested NER and traditional (i.e., flat) NER. We then review the existing nested NER approaches from 2002 to 2020 and mainly classify them into five categories according to the model architecture, including early rule-based, layered-based, region-based, hypergraph-based, and transition-based approaches. We also explore in greater depth the impact of key properties unique to nested NER approaches from the model property perspective, namely entity dependency, stage framework, error propagation, and tag scheme. Finally, we summarize the open challenges and point out a few possible future directions in this area. This survey would be useful for three kinds of readers: (i) Newcomers in the field who want to learn about NER, especially for nested NER. (ii) Researchers who want to clarify the relationship and advantages between flat NER and nested NER. (iii) Practitioners who just need to determine which NER technique (i.e., nested or not) works best in their applications.

Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish

“…M0 and M1 mentions were recognized using the Multiple LSTM-CRF (MLC) architecture, 48 an adaptation of the model proposed by Lample et al 49 to solve the nested NER task. 50 Specifically, they used a Bi-LSTM and CRF layers to recognize each entity type and incorporated pretrained embeddings trained on the Chilean Waiting List corpus 44 , 51 and character-level contextualized embeddings. 52 The code is freely available.…”

A Chinese nested named entity recognition approach using sequence labeling

“…Similarly, the words “北(Bei)”, “京(Jing)”, “石(Shi)”, “景(Jing)” and “山(Shan)” have two entity labels representing different categories. If these nested named entities can be extracted accurately, they can serve as assistant features to improve the performance in knowledge graph, intelligent question-answering and some other applications (Sellami and Zarour, 2022; Ashtiani and Raahmei, 2023; Wang et al , 2022).…”

Biomedical named entity recognition based on fusion multi-features embedding

“…So, it is very important to quickly and efficiently extract disease-related information. The Biomedical Named Entity Recognition (BNER) is the first step and the most important step in biomedical semantic information extraction [ 1 , 2 , 3 ]. BNER is a prerequisite and critical part of building medical knowledge graphs, medical question and answer systems, medical text classification in biomedical field [ 4 , 5 ].…”

“…The deep learning approach uses end-to-end model training and automatic feature extraction to avoid tedious manual extraction by acquiring features and distributed data representations with good generalization capabilities. Compared to the lexicon and rule-based methods or statistical machine learning methods, deep learning neural network-based methods have the advantage of no longer relying on manual features and domain knowledge, reducing the cost of manual feature extraction, having more robust generalization, and effectively improving system efficiency [ 2 , 3 , 4 ].…”