A comparative study of deep learning based language representation learning models
Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representati...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-05-01.
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| Online Access: | Get fulltext |
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| LEADER | 02161 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24400_15001 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Boukabous, Mohammed |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Azizi, Mostafa |e author |
| 245 | 0 | 0 | |a A comparative study of deep learning based language representation learning models |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-05-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24400 | ||
| 520 | |a Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. In this paper, we highlight the most important language representation learning models in NLP and provide an insight of their evolution. We also summarize, compare and contrast these different models on sentiment analysis, and thus discuss their main strengths and limitations. Our obtained results show that BERT is the best language representation learning model. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science; Natural Language Processing; Deep Learning | ||
| 690 | |a Natural Language Processing; Representation Models; BERT; GPT-2; Deep Learning; Sentiment Analysis | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 2: May 2021; 1032-1040 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24400/15001 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24400/15001 |z Get fulltext |