Large-Scale Text Classification with Deep Neural Networks
The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long- Short Term Memory...
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| Vydáno v: | KIISE Transactions on Computing Practices Ročník 23; číslo 5; s. 322 - 327 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Korean Institute of Information Scientists and Engineers
15.05.2017
한국정보과학회 |
| Témata: | |
| ISSN: | 2383-6318, 2383-6326 |
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| Abstract | The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long- Short Term Memory (LSTM) and Gated Recurrent Units (GRU). The experiment’s result revealed that the performance of classification algorithms was Multinomial Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows: First, the result of CNN was better than LSTM. Therefore, the text classification problem might be related more to feature extraction problem than to natural language understanding problems. Second, judging from the results the GRU showed better performance in feature extraction than LSTM. Finally, the result that the GRU was better than CNN implies that text classification algorithms should consider feature extraction and sequential information. We presented the results of fine-tuning in deep neural networks to provide some intuition regard natural language processing to future researchers. KCI Citation Count: 4 |
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| AbstractList | The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long- Short Term Memory (LSTM) and Gated Recurrent Units (GRU). The experiment’s result revealed that the performance of classification algorithms was Multinomial Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows: First, the result of CNN was better than LSTM. Therefore, the text classification problem might be related more to feature extraction problem than to natural language understanding problems. Second, judging from the results the GRU showed better performance in feature extraction than LSTM. Finally, the result that the GRU was better than CNN implies that text classification algorithms should consider feature extraction and sequential information. We presented the results of fine-tuning in deep neural networks to provide some intuition regard natural language processing to future researchers. KCI Citation Count: 4 |
| Author | Jae-Hong Eom(엄재홍) Jeong-Ho Chang(장정호) Hwiyeol Jo(조휘열) Jin-Hwa Kim(김진화) Kyung-Min Kim(김경민) Byoung-Tak Zhang(장병탁) |
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| Keywords | deep learning artificial neural networks 자연어 처리 인공신경망 대용량 문서 분류 natural language processing large-scale text classification 딥러닝 |
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| Title | Large-Scale Text Classification with Deep Neural Networks |
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