The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec
Abstract
Abstract The purpose of document classification is to assign the most appropriate label to a specified document. The main challenges in document classification are insufficient label information and unstructured sparse format. A semi-supervised learning (SSL) approach could be an effective solution to the former problem, whereas the consideration of multiple document representation schemes can resolve the latter problem. Co-training is a popular SSL method that attempts to exploit various perspectives in terms of feature subsets for the same example. In this paper, we propose multi-co-training (MCT) for improving the performance of document classification. In order to increase the variety of feature sets for classification, we transform a document using three document representation methods: term frequency–inverse document frequency (TF–IDF) based on the bag-of-words scheme, topic distribution based on latent Dirichlet allocation (LDA), and neural-network-based document embedding known as document to vector (Doc2Vec). The experimental results demonstrate that the proposed MCT is robust to parameter changes and outperforms benchmark methods under various conditions.
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Thumbs up? Sentiment Classification using Machine Learning Techniques
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Proceedings of the 29th International Conference on Machine Learning (ICML-12)
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Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting
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“Consequently, higher IDF values indicate rare phrases in the whole set, thereby enhancing their overall significance [19,20].”
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Metadata
- Authors
- Kim, D., Seo, D., Cho, S., & Kang, P.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 477
- Pages
- 15-29
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2897098357
- Cited by
- 426
- References
- 32
- FWCI
- 22.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kim, D., Seo, D., Cho, S., & Kang, P. (2019). Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec. Information Sciences. https://doi.org/10.1016/j.ins.2018.10.006
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