Deep Learning in Natural Language Processing

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ASIN ‏ : ‎ B07D8PF8LF
Publisher ‏ : ‎ Springer; 1st ed. 2018 edition (May 23, 2018)
Publication date ‏ : ‎ May 23, 2018
Language ‏ : ‎ English
File size ‏ : ‎ 18940 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 350 pages

Deep Learning in Natural Language Processing

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. Deep learning, a subset of machine learning, has revolutionized the way NLP tasks are approached and solved.

Deep learning models, such as neural networks, have shown remarkable success in various NLP tasks, including sentiment analysis, machine translation, speech recognition, and text generation. These models are able to learn complex patterns and relationships in text data, allowing them to make accurate predictions and generate human-like responses.

One of the key advantages of deep learning in NLP is its ability to automatically extract features from raw text data, eliminating the need for manual feature engineering. This not only saves time and resources but also enables the development of more flexible and adaptable NLP systems.

Furthermore, deep learning models can be trained on large amounts of text data, which helps improve their performance and generalization capabilities. This has led to significant advancements in NLP research and applications, making it possible to develop more sophisticated and intelligent language processing systems.

In conclusion, deep learning has had a profound impact on the field of natural language processing, enabling the development of powerful and robust NLP models. As research in deep learning continues to advance, we can expect even more breakthroughs in NLP technology, ultimately leading to more accurate and efficient language processing systems.
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