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A Survey on Text Classification: From Shallow to Deep Learning
[article]
2021
arXiv
pre-print
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This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. ...
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. ...
A Survey on Text Classification: From Traditional to Deep Learning 111:21
Table 1 . 1 Basic information based on different models. Trans: Transformer. Time: training time. ...
arXiv:2008.00364v6
fatcat:a6zp52rtf5awlh253yp62wqt3a
Machine learning in construction: From shallow to deep learning
2021
Developments in the Built Environment
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This paper reviews the history of machine learning development from shallow to deep learning and its applications in construction. ...
However, the implementation of machine learning faces a range of challenges due to the difficulties in acquiring labeled data, especially when applied in a highly complex construction site environment. ...
Text mining and miscellaneous Text mining refers to the acquisition of valuable information and knowledge from text data, it is a method of data mining. ...
doi:10.1016/j.dibe.2021.100045
fatcat:5ntebbgk45afjjfozvpkiqjdri
A Survey on Concept Factorization: From Shallow to Deep Representation Learning
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2007.15840v1 fatcat:rq2yvtrcqfhnhjsb5wjiahba4iOther Versions
Specifically, we first re-view the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. ...
As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. ...
Thus, in this survey paper, we aim to present a comprehensive survey on the concept factorization algorithms. ...
arXiv:2007.15840v3
fatcat:ahun2mogmfapxe4mqhqlsakyku
A Survey on Text Classification Algorithms: From Text to Predictions
2022
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This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. ...
To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel ...
Acknowledgments: We would like to thank NIST for allowing us to utilise the RCV1 dataset in our experiments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/info13020083
fatcat:ow4ya53bsbevdjoxwhrq2bz7xy
From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome
2022
Human Genomics
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In this paper, we survey the results from a subset of published work in application of machine learning in the recognition of genomic signals and regions in human genome and summarize some lessons learnt ...
applications, and finally, new machine learning paradigms, of which deep learning is the most well-known example. ...
Acknowledgements The authors would like to thank Dr. Magbubah Essack for the discussion on cis-regulatory elements. ...
doi:10.1186/s40246-022-00376-1
pmid:35180894
pmcid:PMC8855580
fatcat:suvz5fd5i5frxkfq2p73hzau2a
A Survey on Text Classification: From Traditional to Deep Learning
2022
ACM Transactions on Intelligent Systems and Technology
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This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. ...
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. ...
A Survey on Text Classification 31:5
KNN-based Methods. ...
doi:10.1145/3495162
fatcat:ehrzpu4eezf7lah6jm3gyksyaq
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
[article]
2019
arXiv
pre-print
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This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately ...
, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and ...
On the one hand, several authors have proposed approaches for automatically extracting missing knowledge graph triples from text [RYM10] . ...
arXiv:1912.06612v1
fatcat:yfnx3pzs6jhxtggaylc76pwjc4
A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts
2021
Applied Sciences
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Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. ...
This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. ...
Text classification [13] is one of the most common applications for semisupervised learning. ...
doi:10.3390/app11188319
fatcat:notb6zimcvfxhhuaik73t75sje
A survey on big data analytics with deep learning in text using machine learning mechanisms
2018
International Journal of Engineering & Technology
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In this paper, we introduce a framework of Deep learning in ML on big data (DLiMLBiD) to guide the discussion of its opportunities and challenges. ...
In this paper we are providing the review of different Deep learning in text using Machine Learning and Big data methods. ...
classifiers and its performance is best in terms of classification and text mining using Deep learning techniques. ...
doi:10.14419/ijet.v7i2.21.12398
fatcat:nm6p2yv2mzax7jmebea44mw7cy
Towards Automated Website Classification by Deep Learning
[article]
2021
arXiv
pre-print
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Essentially, we tackle a text classification task: an algorithm must learn to infer whether an Italian enterprise performs e-commerce from the textual content of its website. ...
text classification tasks. ...
Train a machine learning algorithm to learn how to predict a survey variable Y (e.g. whether the enterprise has e-commerce facilities deployed on its own website) using, as input information X, the text ...
arXiv:1910.09991v2
fatcat:khotuyt64jei7i4betrts7gvb4
Text Classification Algorithms: A Survey
2019
Information
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In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts ...
In thispaper, a brief overview of text classification algorithms is discussed. ...
Acknowledgments: The authors would like to thank Matthew S. Gerber for his feedback and comments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/info10040150
fatcat:qfmjtzsaoreahdwdwlfhymjtru
Facebook text posts classification with TensorFlow
2019
Mathematical machines and systems
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PyTorch quickly attracted attention from researchers and teachers; BigDL applies deep learning on Spark clusters. ...
the classification of texts. ...
doi:10.34121/1028-9763-2019-3-47-54
fatcat:ivjbfdol6rgfnpwnflqeje6v3e
A Survey of Active Learning for Text Classification using Deep Neural Networks
[article]
2020
arXiv
pre-print
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We review AL for text classification using deep neural networks (DNNs) and elaborate on two main causes which used to hinder the adoption: (a) the inability of NNs to provide reliable uncertainty estimates ...
Moreover, we review recent NN-based advances in NLP like word embeddings or language models in the context of (D)NNs, survey the current state-of-the-art at the intersection of AL, text classification, ...
on recent advances in language models. ...
arXiv:2008.07267v1
fatcat:joainuwblzbaplbls54tq4do3u
Multimodal Classification: Current Landscape, Taxonomy and Future Directions
[article]
2021
arXiv
pre-print
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We address these challenges by proposing a new taxonomy for describing such systems based on trends found in recent publications on multimodal classification. ...
Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine. ...
Co-learning is a form of transfer learning where data from one modality can be used to boost the other. ...
arXiv:2109.09020v1
fatcat:yagsbnxeefcpneqwgflrxxioqa
Multimodal Classification: Current Landscape, Taxonomy and Future Directions
2022
ACM Computing Surveys
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We address these challenges by proposing a new taxonomy for describing multimodal classification models based on trends found in recent publications. ...
Prior research has shown the benefits of combining data from multiple sources compared to traditional unimodal data which has led to the development of many novel multimodal architectures. ...
The increasing interest in multimodal learning has led to a number of recent survey papers covering entire domains [9, 59, 122, 124] , with many of these surveys focusing on deep learning [32, 77, 115 ...
doi:10.1145/3543848
fatcat:ejigpgm5gnabvc4jrb3nml5l4y
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