A Comprehensive Survey of Deep Learning Techniques Natural Language Processing

Abstract

In NLP research, unsupervised or semi-supervised learning techniques are increasingly getting more attention. These learning techniques are capable of learning from data that has not been manually annotated with the necessary answers or by combining non-annotated and annotated data. This essay presents a survey of various natural language processing methods. The discipline of natural language processing, which integrates linguistics, artificial intelligence, and computer science, was established to make it easier for computers and human language to communicate with one another. It is, as we can say, relevant psychopathology for the study of computer-human interaction. The understanding of natural language, which entails enabling machines to naturally interpret human language, is one of the many challenges this area faces. Discourse analysis, morphological separation, machine translation, production and understanding of NLP, part-of-speech tagging, recognition of optical characters, speech recognition, and sentiment analysis are some of the most frequent NLP tasks. As opposed to learning, which is supervised and typically yields few correct results for a given amount of input data, this job is typically quite difficult. However, there is a sizable amount of data available that is unannotated in nature, i.e. the entire contents are available on the internet, and it typically yields less accurate findings.