Siamese Networks for Large-Scale Author Identification
This addresses the problem of identifying authors from text for applications like forensics or plagiarism detection, but it is incremental as it adapts existing Siamese network methods to a new domain.
The paper tackled authorship attribution for large numbers of authors by applying Siamese networks to learn stylistic similarity, showing that they substantially outperform previous approaches.
Authorship attribution is the process of identifying the author of a text. Approaches to tackling it have been conventionally divided into classification-based ones, which work well for small numbers of candidate authors, and similarity-based methods, which are applicable for larger numbers of authors or for authors beyond the training set; these existing similarity-based methods have only embodied static notions of similarity. Deep learning methods, which blur the boundaries between classification-based and similarity-based approaches, are promising in terms of ability to learn a notion of similarity, but have previously only been used in a conventional small-closed-class classification setup. Siamese networks have been used to develop learned notions of similarity in one-shot image tasks, and also for tasks of mostly semantic relatedness in NLP. We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform previous approaches.