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Academic Articles & Research Papers
Showing 20 of 138 academic articles
Zhihui Song, Xin Zhou, Jinchen Xu, Xiaodong Ding et al. • Scientific Reports
(2024)
Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and languag...
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Open Access (DOAJ)
DOI: 10.1038/s41598-024-63021-y
Raviraju Balappa D, Gautam Kumar Rajput • Shodh Sari
(2025)
Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, ma...
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Open Access (DOAJ)
DOI: https://doi.org/10.59231/SARI7840
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications
(2022)
Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...
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DOI: https://doi.org/10.1007/s12243-022-00926-7
Rafael A. Calvo, David Milne, M. Sazzad Hussain, Helen M Christensen • Natural Language Engineering
(2017)
Abstract Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways ...
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DOI: https://doi.org/10.1017/s1351324916000383
Diane J. Litman • Proceedings of the AAAI Conference on Artificial Intelligence
(2016)
Advances in natural language processing (NLP) and educational technology, as well as the availability of unprecedented amounts of educationally-relevant text and speech data, have led to an increasing interest in using NLP to address the ne...
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Open Access
DOI: https://doi.org/10.1609/aaai.v30i1.9879
V Geetha, C.K. Gomathy, Mr.D.Sri Datta Vallab Yaratha Yagn, Sai Praneesh • INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
(2023)
Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-ba...
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Open Access
DOI: https://doi.org/10.55041/ijsrem27094
Yijun Xiao, William Yang Wang • Proceedings of the AAAI Conference on Artificial Intelligence
(2019)
Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...
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Open Access
DOI: https://doi.org/10.1609/aaai.v33i01.33017322
Renáta Németh, Júlia Koltai • Intersections
(2023)
Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outsid...
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DOI: https://doi.org/10.17356/ieejsp.v9i1.871
Stéphane M. Meystre, Peter John Haug • BMC Medical Informatics and Decision Making
(2005)
BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However,...
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DOI: https://doi.org/10.1186/1472-6947-5-30
PMC: 1208893
Joydeep Bhattacharjee • Apress eBooks
(2019)
In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or cat...
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DOI: https://doi.org/10.1007/978-1-4842-5121-8_5
Aleš Horák, Adam Rambousek • Routledge eBooks
(2017)
The chapter introduces links between lexicography and natural language processing in both directions. First, how the lexicographic work benefits from techniques of natural language processing such as word sense disambiguation, semantic simi...
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DOI: https://doi.org/10.4324/9781315104942-13
Xavier Vasques • Wiley eBooks
(2024)
This book chapter focuses on natural language processing (NLP) and covers various applications such as classifying messages as spam or ham, sentiment analysis, and the use of Bidirectional Encoder Representations from Transformers (BERT). T...
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DOI: https://doi.org/10.1002/9781394220649.ch4
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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DOI: https://doi.org/10.1162/coli_r_00312
Lauri Karttunen, Kimmo Koskenniemi, van Gerardus Noord (2003)
Finite state methods have been in common use in various areas of natural language processing (NLP) for many years. A series of specialized workshops in this area illustrates this. In 1996, András Kornai organized a very successful workshop...
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Barry Walsh • Apress eBooks
(2022)
Any book on operationalizing Artificial Intelligence today cannot ignore growth in Natural Language Processing (NLP), with market size projected to be $43b in 2025. Ostensibly the branch of AI concerned with programming computers to underst...
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DOI: https://doi.org/10.1007/978-1-4842-8817-7_10
Santanu Pattanayak • Apress eBooks
(2022)
In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...
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DOI: https://doi.org/10.1007/978-1-4842-8931-0_4
Mitchell P. Marcus • Proceedings of the National Academy of Sciences
(1995)
The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. R...
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Open Access
DOI: https://doi.org/10.1073/pnas.92.22.10052
PMC: 40734
Sonit Singh • arXiv (Cornell University)
(2018)
With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1807.02383
Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...
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DOI: https://doi.org/10.1109/icengtechnol.2017.8308186
Alin-Gabriel Vaduva, Simona-Vasilica Oprea, Dragos-Catalin Barbu • Ovidius University Annals: Economic Sciences Series
(2023)
The web offers large volumes of data that is unstructured and fails to be further processed if not
extracted and organized into local variables or into databases. In this paper, we aim to extract data
from the Internet using web scraping ...
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Open Access (DOAJ)