Affiliation:
1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz, NuevaIndustrial Vallejo, Mexico City, Mexico
Abstract
This paper presents a computational model for the unsupervised authorship attribution task based on a traditional machine learning scheme. An improvement over the state of the art is achieved by comparing different feature selection methods on the PAN17 author clustering dataset. To achieve this improvement, specific pre-processing and features extraction methods were proposed, such as a method to separate tokens by type to assign them to only one category. Similarly, special characters are used as part of the punctuation marks to improve the result obtained when applying typed character n-grams. The Weighted cosine similarity measure is applied to improve the B 3 F-score by reducing the vector values where attributes are exclusive. This measure is used to define distances between documents, which later are occupied by the clustering algorithm to perform authorship attribution.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A Study of Medical Decision Recommendation Generation and Similarity Fusion Based on CDSS and ChatGPT-4;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05
2. Towards Improving Multiple Authorship Attribution of Source Code;2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS);2022-12