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Research Topics in Deep Learning for Natural Language Processing

Research Topics in Deep Learning for Natural Language Processing

   In deep learning, Natural Language Processing (NLP) employs computational techniques to analyze and generate human language content automatically. Traditional NLP methods face significant setbacks when employed with shallow machine learning models due to time-consuming and hand-crafted features. The significance of deep learning models for NLP, superior performance even with massive data but require less linguistic expertise to train and operate. The processing of natural language involves morphological analysis, lexical analysis, syntactic analysis, semantic analysis, discourse analysis, and pragmatic analysis.
   Deep Neural Network, Autoencoders, Restricted Boltzmann Machine, Recurrent Neural Network, Long short term memory, General adversarial network, and Convolutional Neural Network are the most commonly used deep learning algorithms for Natural Language Processing. The most popular applications of NLP are machine translation, summarization, Pattern Recognition, question answering, speech recognition, information retrieval and extraction, voice recognition, Text generation, text classification, and sentiment analysis. The advancement and future research in NLP using deep learning involves a combination of deep learning with other technologies such as reinforcement learning, inference, knowledge, and transforms.