An improved N-grams based Model for Authorship Attribution
| Title | An improved N-grams based Model for Authorship Attribution |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | BOUGHACI, Dalila, BENMESBAH, Mounir, ZEBIRI, Aniss |
| Conference Name | 2019 International Conference on Computer and Information Sciences (ICCIS) |
| Date Published | April 2019 |
| Publisher | IEEE |
| ISBN Number | 978-1-5386-8125-1 |
| Keywords | anonymous text, attribution, authorship attribution, automobiles, composability, Computational modeling, corresponding author, Dictionaries, Euclidian distance, Feeds, Human Behavior, improved N-grams based model, Metrics, N-gram, N-grams model, PAN benchmarks, pubcrawl, similarity functions, statistical distributions, text analysis, text categorization, text classification, Text processing |
| Abstract | Authorship attribution is the problem of studying an anonymous text and finding the corresponding author in a set of candidate authors. In this paper, we propose a method based on N-grams model for the problem of authorship attribution. Several measures are used to assign an anonymous text to an author. The different variants of the proposed method are implemented and validated on PAN benchmarks. The numerical results are encouraging and demonstrate the benefit of the proposed idea. |
| URL | https://ieeexplore.ieee.org/document/8716391/ |
| DOI | 10.1109/ICCISci.2019.8716391 |
| Citation Key | boughaci_improved_2019 |
- improved N-grams based model
- Text processing
- text classification
- text categorization
- text analysis
- statistical distributions
- similarity functions
- pubcrawl
- PAN benchmarks
- N-grams model
- N-gram
- Metrics
- anonymous text
- Human behavior
- Feeds
- Euclidian distance
- Dictionaries
- corresponding author
- Computational modeling
- composability
- automobiles
- authorship attribution
- attribution