Natural language processing: state of the art, current trends and challenges

Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answeri...

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Published in	Multimedia tools and applications Vol. 82; no. 3; pp. 3713 - 3744
Main Authors	Khurana, Diksha, Koli, Aditya, Khatter, Kiran, Singh, Sukhdev
Format	Journal Article
Language	English
Published	New York Springer US 01.01.2023 Springer Nature B.V
Subjects	Alliances Automatic summarization Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Discourse analysis Information retrieval Language Linguistics Machine translation Morphology Multimedia Multimedia Information Systems Natural language generation Natural language processing Phonology Semantics Sound Special Purpose and Application-Based Systems Speech Speech recognition Syntax Trends NLP applications NLP evaluation metrics Natural language processing Natural language generation Natural language understanding
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Abstract	Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of N atural L anguage G eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.
AbstractList	Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of N atural L anguage G eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of atural anguage eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.
Author	Koli, Aditya Khurana, Diksha Khatter, Kiran Singh, Sukhdev
Author_xml	– sequence: 1 givenname: Diksha surname: Khurana fullname: Khurana, Diksha organization: Department of Computer Science, Manav Rachna International Institute of Research and Studies – sequence: 2 givenname: Aditya surname: Koli fullname: Koli, Aditya organization: Department of Computer Science, Manav Rachna International Institute of Research and Studies – sequence: 3 givenname: Kiran orcidid: 0000-0002-1000-6102 surname: Khatter fullname: Khatter, Kiran email: kirankhatter@gmail.com organization: Department of Computer Science, BML Munjal University – sequence: 4 givenname: Sukhdev surname: Singh fullname: Singh, Sukhdev organization: Department of Statistics, Amity University Punjab
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35855771$$D View this record in MEDLINE/PubMed
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SubjectTerms	Alliances Automatic summarization Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Discourse analysis Information retrieval Language Linguistics Machine translation Morphology Multimedia Multimedia Information Systems Natural language generation Natural language processing Phonology Semantics Sound Special Purpose and Application-Based Systems Speech Speech recognition Syntax Trends
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Title	Natural language processing: state of the art, current trends and challenges
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