Please use this identifier to cite or link to this item:
Title: Named entity recognition for unaccompanied children based on deep learning
Authors: Yao, Yuxuan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Yao, Y. (2022). Named entity recognition for unaccompanied children based on deep learning. Master's thesis, Nanyang Technological University, Singapore.
Abstract: Since 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.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
2.73 MBAdobe PDFView/Open

Page view(s)

Updated on Jul 12, 2024


Updated on Jul 12, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.