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About 10,000 papers for “text classification”

Text Classification

Ben Elfadhl Mohamed Ahmed, Wahiba Ben Abdessalem Karâa · Advances in computational intelligence and robotics book series · 2017

In this chapter, a supervised automatic text documents classification using the fuzzy decision trees technique is proposed. Whatever the algorithm used in the fuzzy decision trees, there must be a criterion for the choice of discriminating attribute at the nodes to partition. For fuzzy decision trees usually two heuristics were used to select the discriminating attribute at the node to partition. In the field of text documents classification there is a heuristic that has not yet been tested. This chapter tested this heuristic. The latter was presented in the works of Yuan and Shaw (1995) and was applied in a context different then the textual classification. This heuristic is analyzed and adapted to the author's approach for text documents classification.

Cited by 0

Text classification;

Wendy W. Chapman A, Lee M. Christensen B, Michael M. Wagner A et al. · 2004

Cited by 0

Text Classification

Özgür Sahin · Apress eBooks · 2020

Text classification is a natural language processing method that lets us categorize texts into predefined classes. This is a very popular technique that has a variety of use cases. One of the famous uses is classifying text into emotional categories (positive, negative, etc.) which is called sentiment analysis. This method can be used on any text data that has been categorized. Text classification allows us to find the author of a piece of writing, classify GitHub issues, find complaints in App Store reviews, or detect the language of a text. In this chapter, we will learn how to use Create ML and Turi Create to create text classification applications. We will learn by doing example apps. We will develop a spam SMS classifier app with Create ML first and then Turi Create.

Cited by 1

Text Classification

Singh, Gaurav · UCL Discovery (University College London) · 2019

There is an abundance of text data in this world but most of it is raw. We need to extract information from this data to make use of it. One way to extract this information from raw text is to apply informative labels drawn from a pre-defined fixed set i.e. Text Classification. In this thesis, we focus on the general problem of text classification, and work towards solving challenges associated to binary/multi-class/multi-label classification. More specifically, we deal with the problem of (i) Zero-shot labels during testing; (ii) Active learning for text screening; (iii) Multi-label classification under low supervision; (iv) Structured label space; (v) Classifying pairs of words in raw text i.e. Relation Extraction. For (i), we use a zero-shot classification model that utilizes independently learned semantic embeddings. Regarding (ii), we propose a novel active learning algorithm that reduces problem of bias in naive active learning algorithms. For (iii), we propose neural candidate-selector architecture that starts from a set of high-recall candidate labels to obtain high-precision predictions. In the case of (iv), we proposed an attention based neural tree decoder that recursively decodes an abstract into the ontology tree. For (v), we propose using second-order relations that are derived by explicitly connecting pairs of words via context token(s) for improved relation extraction. We use a wide variety of both traditional and deep machine learning tools. More specifically, we used traditional machine learning models like multi-valued linear regression and logistic regression for (i, ii), deep convolutional neural networks for (iii), recurrent neural networks for (iv) and transformer networks for (v).

Cited by 0

Text Classification

Trevor Cohen, Serguei Pakhomov, Amandalynne Paullada et al. · Cognitive informatics in biomedicine and healthcare · 2024

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Text Classification

Dipanjan Sarkar · Apress eBooks · 2019

Learning to process and understand text is one of the first, yet most essential, steps on the journey to getting meaningful insights from textual data. While it is important to understand language syntax, structure, and semantics, it is not sufficient on its own to be able to derive useful patterns and insights and get maximum use out of vast volumes of text data. Knowledge of language processing coupled with concepts from artificial intelligence, machine learning, and deep learning help in building intelligent systems, which can leverage text data and help solve real-world practical problems that benefit businesses and enterprises.

Cited by 2

Efficient text classification

Tan, Cheryl Qian Ru. · DR-NTU (Nanyang Technological University) · 2010

As the digital age pushes forward, data and document size have been increasing rapidly. A more efficient and accurate method of sampling data for training text classifiers is required. We require good samples and not just blind samples from Simple Random Sampling, therefore we experimented on a new proposed sampling algorithm – CONCISE. It is a novel sampling algorithm that is proposed for selecting training documents for text classification and experiments showed that it works particularly well with small sampling ratio. Experiments were conducted on the 20 Newsgroup corpus and Reuters 21578 document set using two classifiers SVM and Naïve Bayes classifier. CONCISE is compared with SRS in all experiments and results showed that CONCISE is consistent in accuracy no matter which classifier is used. In all experiments, CONCISE outperforms SRS in all sampling ratios and the accuracy with CONCISE is higher. However, CONCISE requires more running time but the trade off is small compared to the increase in accuracy.

Cited by 0