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The problem of unveiling the author of a given text document from multiplecandidate authors is called authorship attribution. Manifold word-basedstylistic markers have been successfully used in deep learning methods to dealwith the intrinsic problem of authorship attribution. Unfortunately, theperformance of word-based authorship attribution systems is limited by thevocabulary of the training corpus. Literature has recommended character-basedstylistic markers as an alternative to overcome the hidden word problem.However, character-based methods often fail to capture the sequentialrelationship of words in texts which is a chasm for further improvement. Thequestion addressed in this paper is whether it is possible to address theambiguity of hidden words in text documents while preserving the sequentialcontext of words. Consequently, a method based on bidirectional long short-termmemory (BLSTM) with a 2-dimensional convolutional neural network (CNN) isproposed to capture sequential writing styles for authorship attribution. TheBLSTM was used to obtain the sequential relationship among characteristicsusing subword information. The 2-dimensional CNN was applied to understand thelocal syntactical position of the style from unlabeled input text. The proposedmethod was experimentally evaluated against numerous state-of-the-art methodsacross the public corporal of CCAT50, IMDb62, Blog50, and Twitter50.Experimental results indicate accuracy improvement of 1.07\%, and 0.96\% onCCAT50 and Twitter, respectively, and produce comparable results on theremaining datasets.

Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution