An Effective and Robust Method for Short Text Classification
Abstract
Classification of texts potentially containing a complex and specific terminology requires the use of learning methods that do not rely on extensive feature engineering. In this work we use prediction by partial matching (PPM), a method that compresses texts to capture text features and creates a language model adapted to a particular text. We show that the method achieves a high accuracy of text classification and can be used as an alternative to state-of-art learning algorithms.
Cite
Text
Bobicev and Sokolova. "An Effective and Robust Method for Short Text Classification." AAAI Conference on Artificial Intelligence, 2008.Markdown
[Bobicev and Sokolova. "An Effective and Robust Method for Short Text Classification." AAAI Conference on Artificial Intelligence, 2008.](https://mlanthology.org/aaai/2008/bobicev2008aaai-effective/)BibTeX
@inproceedings{bobicev2008aaai-effective,
title = {{An Effective and Robust Method for Short Text Classification}},
author = {Bobicev, Victoria and Sokolova, Marina},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2008},
pages = {1444-1445},
url = {https://mlanthology.org/aaai/2008/bobicev2008aaai-effective/}
}