From Plagiarism Detection to Bible Analysis: The Potential of Machine Learning for Grammar-Based Text Analysis
Abstract
The amount of textual data available from digitalized sources such as free online libraries or social media posts has increased drastically in the last decade. In this paper, the main idea to analyze authors by their grammatical writing style is presented. In particular, tasks like authorship attribution, plagiarism detection or author profiling are tackled using the presented algorithm, revealing promising results. Thereby all of the presented approaches are ultimately solved by machine learning algorithms.
Cite
Text
Tschuggnall and Specht. "From Plagiarism Detection to Bible Analysis: The Potential of Machine Learning for Grammar-Based Text Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_27Markdown
[Tschuggnall and Specht. "From Plagiarism Detection to Bible Analysis: The Potential of Machine Learning for Grammar-Based Text Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/tschuggnall2016ecmlpkdd-plagiarism/) doi:10.1007/978-3-319-46131-1_27BibTeX
@inproceedings{tschuggnall2016ecmlpkdd-plagiarism,
title = {{From Plagiarism Detection to Bible Analysis: The Potential of Machine Learning for Grammar-Based Text Analysis}},
author = {Tschuggnall, Michael and Specht, Günther},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {245-248},
doi = {10.1007/978-3-319-46131-1_27},
url = {https://mlanthology.org/ecmlpkdd/2016/tschuggnall2016ecmlpkdd-plagiarism/}
}