Authorship Analysis based on Data Compression
This work addresses authorship attribution for heterogeneous texts, but it is incremental as it builds on existing compression-based methods with a novel similarity measure.
The authors tackled authorship analysis by using Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries from texts, and found that results are comparable to state-of-the-art methods and outperform traditional compression-based approaches across documents in five languages and different historical periods.
This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts. The FCD computes a similarity between two documents through an effective binary search on the intersection set between the two related dictionaries. In the reported experiments the proposed method is applied to documents which are heterogeneous in style, written in five different languages and coming from different historical periods. Results are comparable to the state of the art and outperform traditional compression-based methods.