Cross-Genre Authorship Verification Using Unmasking
Bibliographic Data
| ID | 8862044 |
|---|---|
| Authors | Mike Kestemont (0000-0003-3590-693X, corresponding author), Kim Luyckx (0000-0003-2047-2251), Walter Daelemans (0000-0002-9832-7890), Thomas Crombez |
| Year | 2012 |
| Volume | 93 |
| Issue | 3 |
| Pages | 340-356 |
| Publication date | 2012-05-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | English Studies (JOURNAL) |
| Journal identifiers | ISSN: 0013-838X • E-ISSN: 1744-4217 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/0013838x.2012.668793 |
| OpenAlex | W2139445257 |
| Language | EN |
| Citations received | 5 |
| References cited | 16 |
In this paper we will stress-test a recently proposed technique for computational authorship verification, ‘‘unmasking'', which has been well received in the literature. The technique envisages an experimental set-up commonly referred to as ‘‘authorship verification'', a task generally deemed more difficult than so-called ‘‘authorship attribution''. We will apply the technique to authorship verification across genres, an extremely complex text categorization problem that so far has remained unexplored. We focus on five representative contemporary English-language authors. For each of them, the corpus under scrutiny contains several texts in two genres (literary prose and theatre plays). Our research confirms that unmasking is an interesting technique for computational authorship verification, especially yielding reliable results within the genre of (larger) prose works in our corpus. Authorship verification, however, proves much more difficult in the theatrical part of the corpus
Authorship attribution · Categorization · Focus (optics · Information retrieval · Linguistics · Natural language processing · Plagiarism detection · Programming language · Scrutiny · Set (abstract data type · Task (project management · Authorship Attribution and Profiling · Computer Science · Engineering · Hate Speech and Cyberbullying Detection · Topic Modeling · Artificial Intelligence · Philosophy
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,36 |
| Citation span | 2012 - 2025 (14) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |