Cluster and dynamic-TrAdaBoost-based transfer learning for text classification

2017 ◽  
Author(s):  
Zibin Li ◽  
Bo Liu ◽  
Yanshan Xiao
Keyword(s):  
Transfer Learning ◽  
Text Classification

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Transfer Learning for Spam Text Classification

2021 ◽  
Vol 9 (VIII) ◽  
pp. 638-641
Author(s):  
Pratiksha Bongale
Keyword(s):  
Transfer Learning ◽  
Text Classification ◽  
Data Transfer ◽  
The Other ◽  
Training Data ◽  
Data Driven ◽  
Specific Training ◽  
Text Data ◽  
Domain Specific ◽  
Corpus Data

Today’s world is mostly data-driven. To deal with the humongous amount of data, Machine Learning and Data Mining strategies are put into usage. Traditional ML approaches presume that the model is tested on a dataset extracted from the same domain from where the training data has been taken from. Nevertheless, some real-world situations require machines to provide good results with very little domain-specific training data. This creates room for the development of machines that are capable of predicting accurately by being trained on easily found data. Transfer Learning is the key to it. It is the scientific art of applying the knowledge gained while learning a task to another task that is similar to the previous one in some or another way. This article focuses on building a model that is capable of differentiating text data into binary classes; one roofing the text data that is spam and the other not containing spam using BERT’s pre-trained model (bert-base-uncased). This pre-trained model has been trained on Wikipedia and Book Corpus data and the goal of this paper is to highlight the pre-trained model’s capabilities to transfer the knowledge that it has learned from its training (Wiki and Book Corpus) to classifying spam texts from the rest.

Quadruple Transfer Learning: Exploiting both shared and non-shared concepts for text classification

2015 ◽  
Vol 90 ◽  
pp. 199-210 ◽  
Author(s):  
Jianhan Pan ◽  
Xuegang Hu ◽  
Yuhong Zhang ◽  
Peipei Li ◽  
Yaojin Lin ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Text Classification

scholarly journals Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer

2017 ◽  
Author(s):  
Seungwhan Moon ◽  
Jaime Carbonell
Keyword(s):  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
A Priori ◽  
Classification Task ◽  
Learning Approach ◽  
Learning Framework ◽  
Previous State

We study a transfer learning framework where source and target datasets are heterogeneous in both feature and label spaces. Specifically, we do not assume explicit relations between source and target tasks a priori, and thus it is crucial to determine what and what not to transfer from source knowledge. Towards this goal, we define a new heterogeneous transfer learning approach that (1) selects and attends to an optimized subset of source samples to transfer knowledge from, and (2) builds a unified transfer network that learns from both source and target knowledge. This method, termed "Attentional Heterogeneous Transfer", along with a newly proposed unsupervised transfer loss, improve upon the previous state-of-the-art approaches on extensive simulations as well as a challenging hetero-lingual text classification task.

scholarly journals Bilingual Text Classification in English and Indonesian via Transfer Learning using XLM-RoBERTa

2021 ◽  
Vol 13 (3) ◽  
pp. 73-87
Author(s):  
Yakobus Wiciaputra ◽  
Julio Young ◽  
Andre Rusli
Keyword(s):  
Correlation Coefficient ◽  
Transfer Learning ◽  
Language Processing ◽  
Text Classification ◽  
Classification Model ◽  
Training Dataset ◽  
The Internet ◽  
Matthew Correlation Coefficient ◽  
Text Information ◽  
Multilingual Text

With the large amount of text information circulating on the internet, there is a need of a solution that can help processing data in the form of text for various purposes. In Indonesia, text information circulating on the internet generally uses 2 languages, English and Indonesian. This research focuses in building a model that is able to classify text in more than one language, or also commonly known as multilingual text classification. The multilingual text classification will use the XLM-RoBERTa model in its implementation. This study applied the transfer learning concept used by XLM-RoBERTa to build a classification model for texts in Indonesian using only the English News Dataset as a training dataset with Matthew Correlation Coefficient value of 42.2%. The results of this study also have the highest accuracy value when tested on a large English News Dataset (37,886) with Matthew Correlation Coefficient value of 90.8%, accuracy of 93.3%, precision of 93.4%, recall of 93.3%, and F1 of 93.3% and the accuracy value when tested on a large Indonesian News Dataset (70,304) with Matthew Correlation Coefficient value of 86.4%, accuracy, precision, recall, and F1 values of 90.2% using the large size Mixed News Dataset (108,190) in the model training process. Keywords: Multilingual Text Classification, Natural Language Processing, News Dataset, Transfer Learning, XLM-RoBERTa

A Comparison of Methods in Political Science Text Classification: Transfer Learning Language Models for Politics

2020 ◽  
Author(s):  
Zhanna Terechshenko ◽  
Fridolin Linder ◽  
Vishakh Padmakumar ◽  
Fengyuan Liu ◽  
Jonathan Nagler ◽  
...  
Keyword(s):  
Political Science ◽  
Transfer Learning ◽  
Text Classification ◽  
Language Models ◽  
Comparison Of Methods ◽  
Science Text ◽  
Learning Language
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