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Text Generation for Imbalanced Text Classification
2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2019The 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 +2 more
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Concatenate text embeddings for text classification
2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), 2017Text 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
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Multilingual Text Classification Using Ontologies
2007In this paper, we investigate strategies for automatically classifying documents in different languages thematically, geographically or according to other criteria. A novel linguistically motivated text representation scheme is presented that can be used with machine learning algorithms in order to learn classifications from pre-classified examples and
de Melo, G., Siersdorfer, S.
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Transductive Text Classification
2002For many practical uses of text classification, it is crucial that the learner be able to generalize well using little training data. A news-filtering service, for example, requiring a hundred days’ worth of training data is unlikely to please even the most patient users.
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2002
After giving the theoretical motivation and justification for the maximum-margin approach to text classification in the previous two chapters, this chapter evaluates its empirical performance. It also addresses practical issues related to selecting a good representation and an appropriate parameter setting.
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After giving the theoretical motivation and justification for the maximum-margin approach to text classification in the previous two chapters, this chapter evaluates its empirical performance. It also addresses practical issues related to selecting a good representation and an appropriate parameter setting.
openaire +1 more source