Named Entity Recognition in User-Generated Text: A Systematic Literature Review

Named Entity Recognition (NER) in social media has received much research attention in the field of natural language processing (NLP) and information extraction. Research on this topic has grown dramatically in recent years. Hence, one of the objectives of this systematic literature review (SLR) is...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.136330-136353
Main Authors: Esmaail, Naji, Omar, Nazlia, Mohd, Masnizah, Fauzi, Fariza, Mansur, Zainab
Format: Article
Language:English
Subjects:
Blogs
information extraction
Information retrieval
Named entity recognition
Natural language processing
NER
Reviews
SLR
social media
Social networking (online)
Surveys
systematic literature review
Systematics
user-generated text
WNUT
Citations: Items that this one cites
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Summary:Named Entity Recognition (NER) in social media has received much research attention in the field of natural language processing (NLP) and information extraction. Research on this topic has grown dramatically in recent years. Hence, one of the objectives of this systematic literature review (SLR) is to present the outline techniques, approaches, and methods used to handle NER on X based on English datasets prepared for WNUT (Workshop on User-generated Text). This study could be used to develop more accurate models in the future. This SLR focuses on articles that had been published over the course of eight years, i.e., from July 2015 to the end of 2023. A total of 67 out of 316 articles published during the period were selected having met the set chosen criteria. Based on the analysis of the selected articles, challenges were identified and discussed. In this SLR, we aim to provide a better understanding of current viewpoints and highlight opportunities for research in NER in User-generated Text specifically for English usage on X. It can aid in identifying named entities, such as names, locations, companies, and groups, within a specific informal social media context like X. This research is notable for being the first systematic review that emphasizes the dearth of NER on X based on English datasets prepared for WNUT.The main contribution of this systematic review is a comprehensive study on NER in X messages for social media, entailing its challenges and opportunities. Moreover, new possible research directions are suggested for the researchers.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3427714