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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Bibliographic Details
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
Extraction
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
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-13428-4