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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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/156760 |
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| 總結: | 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. |
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