AI-Natural Language Processing (NLP)

2021 ◽  
Vol 9 (VIII) ◽  
pp. 135-140
Author(s):  
Miss. Aliya Anam Shoukat Ali
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Question Answering ◽  
Text Processing ◽  
Human Language ◽  
Spell Check ◽  
Email Spam

Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

scholarly journals An Analysis of the Applications of Natural Language Processing in Various Sectors

2021 ◽  
Author(s):  
Priya B ◽  
Nandhini J.M ◽  
Gnanasekaran T
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Health Sector ◽  
Automated System ◽  
Human Language ◽  
Agriculture Sector ◽  
Prediction Of Diabetes

Natural Language processing (NLP) dealing with Artificial Intelligence concept is a subfield of Computer Science, enabling computers to understand and process human language. Natural Language Processing being a part of artificial intelligence provides understanding of human language by computers for the purpose of extracting information or insights and create meaningful response. It involves creating algorithms that transform text in to words labeling With the emerging advancements in Machine learning and Deep Learning, NLP can contributed a lot towards health sector, education, agriculture and so on. This paper summarizes the various aspects of NLP along with case studies associated with Health Sector for Voice Automated System, prediction of Diabetes Millets, Crop Detection technique in Agriculture Sector.

scholarly journals An Introduction to Natural Language Processing: the Main Problems

Triangle ◽  
2018 ◽  
pp. 65
Author(s):  
Veronica Dahl
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Human Language ◽  
Language Understanding ◽  
Written Text

Natural Language Processing aims to give computers the power to automatically process human language sentences, mostly in written text form but also spoken, for various purposes. This sub-discipline of AI (Artificial Intelligence) is also known as Natural Language Understanding.

scholarly journals Natural Language Processing: Components, Advances, Tools and Industrial Applications

2021 ◽  
Vol 9 (9) ◽  
pp. 964-971
Author(s):  
Hima Yeldo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Industrial Applications ◽  
Spam Detection ◽  
Language Understanding ◽  
Language Generation ◽  
Email Spam

Abstract: Natural Language Processing is the study that focuses the interplay between computer and the human languages NLP has spread its applications in various fields such as an email Spam detection, machine translation, summation, information extraction, and question answering etc. Natural Language Processing classifies two parts i.e. Natural Language Generation and Natural Language understanding which evolves the task to generate and understand the text.

Natural language processing and the Now-or-Never bottleneck

2016 ◽  
Vol 39 ◽  
Author(s):  
Carlos Gómez-Rodríguez
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Cognitive Science ◽  
Computational Linguistics ◽  
Language Processing ◽  
Human Language ◽  
Additional Support ◽  
Scale Data

AbstractResearchers, motivated by the need to improve the efficiency of natural language processing tools to handle web-scale data, have recently arrived at models that remarkably match the expected features of human language processing under the Now-or-Never bottleneck framework. This provides additional support for said framework and highlights the research potential in the interaction between applied computational linguistics and cognitive science.

scholarly journals Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop)

2020 ◽  
Vol 4 (1) ◽  
pp. 208
Author(s):  
Albert Yakobus Chandra ◽  
Didik Kurniawan ◽  
Rahmat Musa
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Services ◽  
Human Language ◽  
Coffee Shop ◽  
The Impact

Some cases that are often experienced at a particular institution such as Micro Enterprise are often a staff / employee in providing information services and transactions that are carried out manually to customers related to these business activities. This cycle always repeats from one customer to another. The impact if there are conditions where the queue of customer that is quite crowded than the workload of staff/employees will be higher and the risk of error in transactions will be high too. The development of information technology in artificial intelligence on 4.0 industry era is moving forward. One of them is Machine Learning - Natural Language Processing (NLP) which is one of the sciences that focuses on how computers can understand the human language and response to it. Therefor in this research a chatbot system will be builtin providing information and conducting transaction with the customers. This chatbot will be develop using the Dialogflow tools provided by Google. This Chatbot that was build expected to be an alternative that can be implemented in various bussines to provide better service for customers

scholarly journals Trends In Natural Language Processing : Scope And Challenges

2021 ◽  
pp. 393-401
Author(s):  
Shreyashi Chowdhury ◽  
Asoke Nath
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Human Language ◽  
Statistical Machine Learning ◽  
Language Understanding ◽  
Current Trends ◽  
Language Data ◽  
Voice Data

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.NLP combines computational linguistics—rule-based modelling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. This paper discusses on the various scope and challenges , current trends and future scopes of Natural Language Processing.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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