Abstract
Natural Language Processing using Deep Learning is one of the critical areas of Artificial Intelligence to focus in the next decades. Over the last few years, Artificial intelligence had evolved by maturing critical areas in research and development. The latest developments in Natural Language Processing con- tributed to the successful implementation of machine translations, linguistic models, Speech recognitions, automatic text generations applications. This paper covers the recent advancements in Natural Language Processing using Deep Learning and some of the much-waited areas in NLP to look for in the next few years. The first section explains Deep Learning architecture, Natural Language Processing techniques followed by the second section that highlights the developments in NLP using Deep learning and the last part by concluding the critical takeaways from my article.
Highlights
Natural language processing(NLP) is a field of Artificial Intelligence where computer performs human-like activities such as understanding the meaning of the text, translate one language to other, recognize speech and convert to meaningfulactions, generate and summarize text, the sentiment of a topic,web search, segmentation of documents, radiology reports etc.Developing NLP applications is never an easy task as ma- chines expect humans to code them via programming language [1]
Traditional approaches in understanding human speech using programming languages often underperform because there are many complex areas to handle, such as dialects, the context of the topic and jargons used by speaker [1]
In this article, I presented a detailed survey of the re- cent advances in Natural Language Processing using Deep Learning
Summary
Natural language processing(NLP) is a field of Artificial Intelligence where computer performs human-like activities such as understanding the meaning of the text, translate one language to other, recognize speech and convert to meaningfulactions, generate and summarize text, the sentiment of a topic,web search, segmentation of documents, radiology reports etc.Developing NLP applications is never an easy task as ma- chines expect humans to code them via programming language [1]. The advances in Deep Learning based NLP has been gaining much traction in recent years, which solves complex tasks that legacy models fail to achieve. State of the art systems developed using Deep Learning framework are capable of outperforming humans, and in most of the cases, these systems can handle where a human takes years to solve. The Recent advancements in computing, deep learning approaches have obtained very high performance across many different NLP tasks. One way to construct knowledge graphs is by developing machine learningor deep learning models that extract the relationship between entities from large amounts of text from the internet [4]. Among all the NLP applications, deep learning based models such as sequence-to-sequence algorithms comprising RNNs have made significant advancements in machine translation in recent times and outperformed traditional approaches [5]
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