TY - JOUR
T1 - Recent text-based research and applications in railways: A critical review and future trends
AU - Dong, Kaitai
AU - Romanov, Igor
AU - Mclellan, Colin
AU - Esen, Ahmet F.
N1 - Funding Information:
This work was supported and funded by Siemens Mobility UK . It was part of the internal research project with the support from University of Leeds.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/11
Y1 - 2022/11
N2 - In the railway industry, a significant amount of data is stored in the textual format. The advanced development of natural language processing and text mining techniques enable automatic knowledge extraction and discovery from such documents. This paper presents a systematic review with quantitative and qualitative analyses to understand the current state of text-based research in the context of railway transport. The paper collects 107 relevant publications in the past decade and identifies different channels for researchers to obtain text data in railways and the corresponding text analysis application use-cases. Moreover, a comprehensive analysis is performed on the state-of-the-art machine learning and natural language processing methods. Four key research directions, namely multilingual NLP, digital maintenance, external data integration, and railway-centred solution pipeline, are identified from Siemens Mobility’s perspective to highlight the most prominent challenges faced in the railway industry.
AB - In the railway industry, a significant amount of data is stored in the textual format. The advanced development of natural language processing and text mining techniques enable automatic knowledge extraction and discovery from such documents. This paper presents a systematic review with quantitative and qualitative analyses to understand the current state of text-based research in the context of railway transport. The paper collects 107 relevant publications in the past decade and identifies different channels for researchers to obtain text data in railways and the corresponding text analysis application use-cases. Moreover, a comprehensive analysis is performed on the state-of-the-art machine learning and natural language processing methods. Four key research directions, namely multilingual NLP, digital maintenance, external data integration, and railway-centred solution pipeline, are identified from Siemens Mobility’s perspective to highlight the most prominent challenges faced in the railway industry.
KW - Critical review
KW - Machine learning
KW - Natural language processing
KW - Railway
KW - Text-based analysis
UR - http://www.scopus.com/inward/record.url?scp=85138038252&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2022.105435
DO - 10.1016/j.engappai.2022.105435
M3 - Article
SN - 0952-1976
VL - 116
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 105435
ER -