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Literature Survey on Deep Learning Topics for Natural Language Processingg

   In Artificial Intelligence (AI), Deep learning achieves state-of-the-art outcomes in numerous applications. Especially in Natural Language Processing (NLP), deep learning imparts a commensurate and unprecedented boost in the performance of various challenging NLP tasks. In recent years, a variety of deep learning models have been utilized for NLP tasks to enhance, accelerate, and automate the text analytics functions and NLP features and also offer superior results in the conversion of unstructured text into valuable data and insights. The most promising advantages of wielding deep learning for NLP are effective feature learning, continued improvement in performance, and more general outcomes via large end-to-end models.
   The sub-divisions of NLP have diverged as fundamental and applicative research. The fundamental category encompasses language modeling, morphological analysis, syntactic processing, and semantic analysis, whereas the applicative research category includes automatic extraction of relevant information from texts, translation of text between languages, summarization of documents, automatic answering of questions, classification, and clustering of documents.
   The fundamental building blocks in the revolution of deep learning in NLP are embedding for distributed representations of linguistic entities, semantic generalization, longer span┬ádeep sequence modeling of natural language, effectively representing linguistic levels using hierarchical networks, an end-to-end deep learning methods to cooperatively deal with many NLP tasks.
   The most recent and impressive applications of deep learning in NLP are question answering, sentiment analysis, conversational language understanding, spoken and text-based dialogue systems, biomedical text mining, lexical analysis and parsing knowledge graph, social computing, machine translation, and General Language Understanding Evaluation (GLUE) benchmark.
   Some of the trending advances in deep learning for NLP are neural-symbolic integration, exploration of better memory models, reinforcement, unsupervised and generative learning, multimodal and multitask learning, and meta-learning. Open limitations in deep learning applications for NLP tasks are complexity, computational cost, and interpretability. Due to the rapid progress in deep learning and NLP, its applicability reaches various domains. Since deep learning for NLP stipulates more research work for further improvement in many real-world applications such as cybersecurity and industrial applications.