An Analysis of Deep Learning and Attention Models for Natural Language Processing Tasks (original) (raw)
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IRJCS:: AM Publications,India, 2020
A Language is a means of communication among humans. It has a structure and is defined by the rules of grammar, which govern the constitution of sentences, clauses and words. Linguistics, the scientific study of language concerns itself with a wide variety of topics such as syntax and semantics. It concerns itself with how the words in a language are combined to form a sentence and how meaning and information are derived from the sentence based on the context. Natural Language processing as a computational discipline has the goal of getting computers to perform tasks, which involve language such as sentiment analysis, parts of speech tagging, machine translation and conversational agents. Classical NLP consisted of the symbolic paradigm, which was based on modelling grammar using theoretical computer science and using rules based on logic. Later on, statistical and probabilistic models became the standard with noisy channel models and Bayesian inference methods being prevalent. In t...
A Survey of the Usages of Deep Learning for Natural Language Processing
IEEE Transactions on Neural Networks and Learning Systems, 2020
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.
Deep Learning for Natural Language Processing
Handbook of Statistics, 2018
Deep learning has emerged as a new area of machine learning research. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. It has been successfully applied to several fields such as images, sounds, text and motion. The techniques developed from deep learning research have already been impacting the research of natural language process. This paper reviews the recent research on deep learning, its applications and recent development in natural language processing.
Are Deep Learning Approaches Suitable for Natural Language Processing?
Lecture Notes in Computer Science, 2016
In recent years, Deep Learning (DL) techniques have gained much attention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understanding, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).
A Survey of the Usages of Deep Learning in Natural Language Processing
2018
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.
Recent Trends in Deep Learning Based Natural Language Processing
Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.
Natural Language Processing Advancements By Deep Learning: A Survey
ArXiv, 2020
Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.