Generalized AutoNLP model for name entity recognition task
Unsupervised pre-trained word embeddings have been widely used in recent studies in the field of Natural Language Processing. After the remarkable achievement obtained by the introduction of BERT in various NLP related tasks, studies had been more focused on deep-learning based approach to represe...
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2022
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sg-ntu-dr.10356-1567602022-07-18T00:24:06Z Generalized AutoNLP model for name entity recognition task Wong, Yung Shen Sinno Jialin Pan School of Computer Science and Engineering sinnopan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Document and text processing Unsupervised pre-trained word embeddings have been widely used in recent studies in the field of Natural Language Processing. After the remarkable achievement obtained by the introduction of BERT in various NLP related tasks, studies had been more focused on deep-learning based approach to represent the raw input sequence of string words. However, there is an uncertainty of these deep-learning based approaches able to convey all the semantic meanings of words and have generalized ability on AutoNLP on name entity recognition related tasks. In this project, we have proposed an architecture of a combination of deep-learning based approach word embeddings, BERT with static word embeddings, GloVe. Experiments are conducted to study the performance of our proposed architecture with BERT word embeddings on AutoNLP name entity recognition tasks. Bachelor of Engineering (Computer Science) 2022-07-18T00:24:06Z 2022-07-18T00:24:06Z 2022 Final Year Project (FYP) Wong, Y. S. (2022). Generalized AutoNLP model for name entity recognition task. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156760 https://hdl.handle.net/10356/156760 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computing methodologies::Document and text processing Wong, Yung Shen Generalized AutoNLP model for name entity recognition task |
description |
Unsupervised pre-trained word embeddings have been widely used in recent studies in
the field of Natural Language Processing. After the remarkable achievement obtained by the
introduction of BERT in various NLP related tasks, studies had been more focused on deep-learning based approach to represent the raw input sequence of string words.
However, there is an uncertainty of these deep-learning based approaches able to convey
all the semantic meanings of words and have generalized ability on AutoNLP on name entity
recognition related tasks. In this project, we have proposed an architecture of a combination of
deep-learning based approach word embeddings, BERT with static word embeddings, GloVe.
Experiments are conducted to study the performance of our proposed architecture with BERT
word embeddings on AutoNLP name entity recognition tasks. |
author2 |
Sinno Jialin Pan |
author_facet |
Sinno Jialin Pan Wong, Yung Shen |
format |
Final Year Project |
author |
Wong, Yung Shen |
author_sort |
Wong, Yung Shen |
title |
Generalized AutoNLP model for name entity recognition task |
title_short |
Generalized AutoNLP model for name entity recognition task |
title_full |
Generalized AutoNLP model for name entity recognition task |
title_fullStr |
Generalized AutoNLP model for name entity recognition task |
title_full_unstemmed |
Generalized AutoNLP model for name entity recognition task |
title_sort |
generalized autonlp model for name entity recognition task |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/156760 |
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1738844941417709568 |