Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159276
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dc.contributor.authorYao, Yuxuanen_US
dc.date.accessioned2022-06-12T12:29:05Z-
dc.date.available2022-06-12T12:29:05Z-
dc.date.issued2022-
dc.identifier.citationYao, Y. (2022). Named entity recognition for unaccompanied children based on deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159276en_US
dc.identifier.urihttps://hdl.handle.net/10356/159276-
dc.description.abstractSince 2020, the pandemic has not only brought huge losses to airlines, but also caused great inconvenience to passengers. Compared with adults, children's travel is more significantly affected. Among them, unaccompanied children who travel by air is facing greater difficulties and challenges. This dissertation mainly uses the python crawler framework to extract and obtain an unaccompanied children dataset from the official websites of world-famous airlines. After labeling the corpus with Label-Studio, popular deep learning based models, LSTM/LSTM-CRF, BiLSTM/BiLSTM-CRF and BERT/BERT-CRF are applied to test the strength of those models in named entity recognition on the newly built unaccompanied children dataset. Experimental study has been conducted and comparisons have been made on this dataset. The performance analysis on those models is reported in the dissertation.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleNamed entity recognition for unaccompanied children based on deep learningen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisor-en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisor2Chen Lihuien_US
dc.contributor.supervisoremailELHCHEN@ntu.edu.sgen_US
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