Facebook text posts classification with TensorFlow
Natural language processing (NLP) is one of the most important technologies of the XXI century. Machine Comprehension is a very interesting but challenging task in both Natural Language Processing (NLP) and artificial intelligent (AI) research. NLP can be applied wherever human-machine interaction i...
Збережено в:
| Дата: | 2019 |
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| Автори: | , , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут проблем математичних машин і систем НАН України
2019
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| Назва видання: | Математичні машини і системи |
| Теми: | |
| Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/162295 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Facebook text posts classification with TensorFlow / О.О. Druzhynin, V.V. Nekhai, O.A. Prila // Математичні машини і системи. — 2019. — № 3. — С. 47–54. — Бібліогр.: 18 назв. — англ. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| Резюме: | Natural language processing (NLP) is one of the most important technologies of the XXI century. Machine Comprehension is a very interesting but challenging task in both Natural Language Processing (NLP) and artificial intelligent (AI) research. NLP can be applied wherever human-machine interaction is needed. Recently, deep learning methods show good results in tasks involving NLP. Standard models can often be used to solve a range of tasks, without the need to apply traditional analytical engineering techniques. The widespread distribution of social networks and the large number of users could give us impressive results, which can further build system interests analysis with a large number of established trust relationships. In this article, we will consider the task of classifying texts in relation to the object under study using the TensorFlow framework. |
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