Similarity Learning for Authorship Verification in Social Media
| Autor: | Steffen Zeiler, Benedikt Boenninghoff, Dorothea Kolossa, Robert M. Nickel |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language Information retrieval Point (typography) Computer science Machine Learning (stat.ML) 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences Authorship verification 01 natural sciences Machine Learning (cs.LG) Variety (cybernetics) Task (project management) Range (mathematics) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Social media Computation and Language (cs.CL) Similarity learning 0105 earth and related environmental sciences |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp.2019.8683405 |
| Popis: | Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not. A range of successful technical approaches has been proposed for this task, many of which are based on traditional linguistic features such as n-grams. These algorithms achieve good results for certain types of written documents like books and novels. Forensic authorship verification for social media, however, is a much more challenging task since messages tend to be relatively short, with a large variety of different genres and topics. At this point, traditional methods based on features like n-grams have had limited success. In this work, we propose a new neural network topology for similarity learning that significantly improves the performance on the author verification task with such challenging data sets. Comment: 5 pages, 3 figures, 1 table, presented on ICASSP 2019 in Brighton, UK |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|