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Academic Articles & Research Papers
Showing 20 of 1,870 academic articles
Carly D. Robinson, Michael Yeomans, Justin Reich, Chris S. Hulleman et al. • International Conference on Learning Analytics and Knowledge (LAK)
(2016)
Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expressio...
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DOI: https://doi.org/10.1145/2883851.2883932
Jonah A. Berger, Grant M. Packard • American Psychologist
(2021)
Language can provide important insights into people, and culture more generally. Further, the digitization of information has made more and more textual data available. But by itself, all that data are just that: data. Realizing its potenti...
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Open Access
DOI: https://doi.org/10.1037/amp0000882
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
Fredrik Olsson • KTH Publication Database DiVA (KTH Royal Institute of Technology)
(2009)
Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain ...
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Nils J. Nilsson • Cambridge University Press eBooks
(2009)
B eyond pattern recognition of individual alphanumeric characters , whether they be of fixed font or handwritten, lies the problem of understanding strings of characters that form words, sentences, or larger assemblages of text in a “natura...
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DOI: https://doi.org/10.1017/cbo9780511819346.009
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
Kevin Crowston, Eileen E. Allen, Robert Heckman • International Journal of Social Research Methodology
(2011)
Social researchers often apply qualitative research methods to study groups and their communications artifacts. The use of computer-mediated communications has dramatically increased the volume of text available, but coding such text requir...
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DOI: https://doi.org/10.1080/13645579.2011.625764
Helmut Schmid • International Conference on Computational Linguistics
(1994)
Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method based on neural networks (Net- Tagger) is presented and its performance is com...
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Open Access
DOI: https://doi.org/10.3115/991886.991915
Yu Zhang, Peter Tiňo B, Aleš Leonardis, Ke Tang • IEEE Transactions on Emerging Topics in Computational Intelligence
(2021)
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...
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Open Access
DOI: https://doi.org/10.1109/tetci.2021.3100641
Martin Sundermeyer, Ralf Schlüter, Hermann Ney • Interspeech (USB)
(2012)
Neural networks have become increasingly popular for the task of language modeling.Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks ca...
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Open Access
DOI: https://doi.org/10.21437/interspeech.2012-65
Danilo P. Mandic, Jonathon A. Chambers • Wiley eBooks
(2001)
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...
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DOI: https://doi.org/10.1002/047084535x
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112
Omid M. Omidvar • Ablex Pub. Corp. eBooks
(1991)
Receptive field calculus, Jan J. Koenderink visual reconstruction and data fusion, D. Suter visual perception of translational and rotational motion, Jim-Shih Liaw, Irwin K. King and Michael A. Arbib neural network for recovering observer m...
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Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
(2019)
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...
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Open Access
DOI: https://doi.org/10.1145/3292500.3330961
J. Lee, Ronald C. Weger, S.K. Sengupta, Ronald M. Welch • IEEE Transactions on Geoscience and Remote Sensing
(1990)
It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...
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DOI: https://doi.org/10.1109/36.58972
Chris Bishop • Choice Reviews Online
(1994)
From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probabi...
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DOI: https://doi.org/10.5860/choice.31-5500
Stamatios V. Kartalopoulos • Institute of Electrical and Electronics Engineers eBooks
(1995)
Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutor...
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DOI: https://doi.org/10.1109/9780470546826
Robert E. Uhrig • Annual Conference of the IEEE Industrial Electronics Society (IECON)
(2002)
A neural network is a data processing system consisting of a large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks ...
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DOI: https://doi.org/10.1109/iecon.1995.483329