Name
Abstract | ||
|---|---|---|
Authorship identification is the task of identifying the author of a given text from a set of suspects. The main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In this paper, deep learning is used for feature extraction of documents represented using variable size character n-grams. We apply A Stacked Denoising AutoEncoder (SDAE) for extracting document features with different settings, and then a support vector machine classifier is used for classification. The results show that the proposed system outperforms its counterparts |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMLA.2016.45 | 2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016) |
Keywords | Field | DocType |
denoising autoencoder, author identification, deep learning | Noise reduction,Pattern recognition,Computer science,Support vector machine classifier,Writing style,Feature extraction,Artificial intelligence,Decoding methods,Deep learning,Artificial neural network,Machine learning,Encoding (memory) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed M. Mohsen | 1 | 0 | 0.34 |
| Nagwa M. El-Makky | 2 | 63 | 11.48 |
| Nagia M. Ghanem | 3 | 95 | 8.18 |