Seow, Wei Liang, Chaturvedi, Iti, Hogarth, Amber, Mao, Rui, and Cambria, Erik (2025) A review of named entity recognition: from learning methods to modelling paradigms and tasks. Artificial Intelligence Review, 58. 315.

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Abstract

Named Entity Recognition (NER) is commonly used when summarising news articles and legal documents. It can extract the names of politicians or organisations and help determine the aspect of a positive or negative sentiment. Previous surveys have only provided a shallow review of NER with respect to a certain datatype. In contrast, here a much deeper coverage of different approaches is provided. First articles with respect to the learning method are discussed, such as supervised or unsupervised. Next, popular models that combine two or more learning methods are introduced in a bottom-up approach. The most popular NER algorithms are compared on a recently crawled 2024 election dataset from Australia. The effect of different parameters such as number of epochs and learning rate is explored. It is concluded that pre-trained NER models are limited in their ability to model new entities and disambiguate their context. Using the sentiment score together with a state space model over entities in a sentence might help overcome these challenges.

Item ID:	86126
Item Type:	Article (Research - C1)
ISSN:	1573-7462
Copyright Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Date Deposited:	17 Jul 2025 02:13
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
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