Results 231 to 240 of about 42,686,202 (279)

Context-Aware Contrastive Representation Learning for Zero-Shot Biomedical Text Classification. [PDF]

Proceedings (IEEE Int Conf Bioinformatics Biomed)
Mukherjee R, Jha K.
europepmc   +1 more source

Migrating birds optimization-based feature selection for text classification. [PDF]

PeerJ Comput Sci
Kaya C, Kilimci ZH, Uysal M, Kaya M.
europepmc   +1 more source

MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification. [PDF]

Heliyon
Agbesi VK, Chen W, Yussif SB, Ukwuoma CC, Gu YH, Al-Antari MA.   +5 more
europepmc   +1 more source

Text classification algorithm of tourist attractions subcategories with modified TF-IDF and Word2Vec. [PDF]

PLoS One
Xiao L, Li Q, Ma Q, Shen J, Yang Y, Li D.   +5 more
europepmc   +1 more source

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The impact of preprocessing on text classification

Information Processing and Management, 2014
Preprocessing is one of the key components in a typical text classification framework. This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy, text domain, text language, and dimension reduction.
Alper Kursat Uysal, Serkan Günal
exaly   +4 more sources

Classification of text documents

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), 1998
Summary: The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems.
Yonghong Li, Anil K. Jain 0001
openaire   +4 more sources

Urdu text classification

Proceedings of the 7th International Conference on Frontiers of Information Technology, 2009
This paper compares statistical techniques for text classification using Naive Bayes and Support Vector Machines, in context of Urdu language. A large corpus is used for training and testing purpose of the classifiers. However, those classifiers cannot directly interpret the raw dataset, so language specific preprocessing techniques are applied on it ...
Abbas Raza Ali, Maliha Ijaz
openaire   +2 more sources

Concatenate text embeddings for text classification

2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), 2017
Text embedding has gained a lot of interests in text classification area. This paper investigates the popular neural document embedding method Paragraph Vector as a source of evidence in document ranking. We focus on the effects of combining knowledge-based with knowledge-free document embeddings for text classification task.
Hamid Machhour, Ismail Kassou
openaire   +1 more source

Text Generation for Imbalanced Text Classification

2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2019
The problem of imbalanced data can be frequently found in the real-world data. It leads to the bias of classification models, that is, the models predict most samples as major classes which are often the negative class. In this research, text generation techniques were used to generate synthetic minority class samples to make the text dataset balanced.
Suphamongkol Akkaradamrongrat, Pornpimon Kachamas, Sukree Sinthupinyo   +2 more
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Feature selection in text classification

2016 24th Signal Processing and Communication Application Conference (SIU), 2016
In recent years, text classification have been widely used. Dimension of text data has increased more and more. Working of almost all classification algorithms is directly related to dimension. In high dimension data set, working of classification algorithms both takes time and occurs over fitting problem.
Durmus Ozkan Sahin, Nurullah Ates, Erdal Kiliç   +2 more
openaire   +3 more sources