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Showing 20 of 962 academic articles
Mary McGee Wood • Science of Computer Programming
(1990)
OPENALEX
Open Access
DOI: https://doi.org/10.1016/0167-6423(90)90067-n
Yuexiong Ding, Jie Ma, Xiaowei Luo • Automation in Construction
(2022)
OPENALEX
DOI: https://doi.org/10.1016/j.autcon.2022.104169
Field Cady • Wiley eBooks
(2017)
This chapter starts with several generic sections about natural language processing (NLP) datasets and big-picture concepts. It focuses on the core NLP concepts, and quick-and-dirty techniques to more complicated ones. The most basic concep...
OPENALEX
DOI: https://doi.org/10.1002/9781119092919.ch16
Sebastian Ruder • Arrow@dit (Dublin Institute of Technology)
(2019)
The current generation of neural network-based natural language processing models excels at learning from large amounts of labelled data. Given these capabilities, natural language processing is increasingly applied to new tasks, new domain...
OPENALEX
Open Access
DOI: https://doi.org/10.13025/17598
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo et al. • Foundations and Trends® in Machine Learning
(2023)
Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be bes...
OPENALEX
Open Access
DOI: https://doi.org/10.1561/2200000096
Palash Goyal, Sumit Kumar Pandey, Karan Jain • Apress eBooks
(2018)
Discover the concepts of deep learning used for natural language processing (NLP) in this practical book, with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2...
OPENALEX
DOI: https://doi.org/10.1007/978-1-4842-3685-7
Alessandro Lenci, Marco Passarotti, Rachele Sprugnoli, Fabio Tamburini • IJCoL
(2025)
DOAJ
Open Access (DOAJ)
Miloš Bogdanović, Milena Frtunić Gligorijević, Jelena Kocić, Leonid Stoimenov • Applied Sciences
(2025)
Various areas of natural language processing (NLP) have greatly benefited from the development of large language models in recent years. This research addresses the challenge of developing efficient tokenizers for transformer-based domain-s...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app15137491
Alexander V. Prokopev, Nadezhda V. Puchkova, Natalya V. Timofeeva, Anna V. Simakova et al. • Север и рынок: формирование экономического порядка
(2025)
This study examines the labor market of the Khanty-Mansi Autonomous Okrug–Yugra. The relevance of the research stems from the region’s strong dependence on natural resource reserves and production volumes, driven by the dominance of the min...
DOAJ
Open Access (DOAJ)
DOI: 10.37614/2220-802X.4.2025.90.004
Łukasz Dębowski • Entropy
(2021)
We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural langu...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/e23091148
Arjun Mohanakumar, Massimiliano Rinaldi, Jasmine Hadj Saadoun, Camilla Lazzi et al. • Applied Food Research
(2026)
Agri-food processing generates large volumes of by-products that remain insufficiently exploited despite their content of phenolic compounds, peptides, polysaccharides, carotenoids, dietary fibres and other functional constituents. These si...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.afres.2026.102511
Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh • Multimedia Tools and Applications
(2022)
OPENALEX
Open Access
DOI: https://doi.org/10.1007/s11042-022-13428-4
PMC: 9281254
Peter Spyns • Methods of Information in Medicine
(1996)
An overview is given of natural language processing applications in medicine. An attempt has been made to enumerate the most important and known international projects and to summarize their goals, principles, methods and results. A section...
OPENALEX
DOI: https://doi.org/10.1055/s-0038-1634681
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
OPENALEX
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DOI: https://doi.org/10.1093/nsr/nwx110
Imed Zitouni • Theory and applications of natural language processing
(2014)
OPENALEX
DOI: https://doi.org/10.1007/978-3-642-45358-8
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis et al. • Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)
(2020)
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conferenc...
OPENALEX
cc-by
DOI: https://doi.org/10.18653/v1/2020.aacl-main.46
Anne Kao, Stephen R. Poteet • Springer eBooks
(2007)
OPENALEX
DOI: https://doi.org/10.1007/978-1-84628-754-1
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen et al. • arXiv (Cornell University)
(2011)
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1103.0398
• IEEE International Multitopic Conference (INMIC)
(2004)
OPENALEX
DOI: https://doi.org/10.1109/inmic.2004.1492945
Akshay Kulkarni, Adarsha Shivananda • Apress eBooks
(2019)
Implement natural language processing applications with Python in this problem-solution book. Here, coding exercises will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identi...
OPENALEX
DOI: https://doi.org/10.1007/978-1-4842-4267-4