  Authorship attribution refers to the task of automatically determining the
author based on a given sample of text. It is a problem with a long history and
has a wide range of application. Building author profiles using language models
is one of the most successful methods to automate this task. New language
modeling methods based on neural networks alleviate the curse of dimensionality
and usually outperform conventional N-gram methods. However, there have not
been much research applying them to authorship attribution. In this paper, we
present a novel setup of a Neural Network Language Model (NNLM) and apply it to
a database of text samples from different authors. We investigate how the NNLM
performs on a task with moderate author set size and relatively limited
training and test data, and how the topics of the text samples affect the
accuracy. NNLM achieves nearly 2.5% reduction in perplexity, a measurement of
fitness of a trained language model to the test data. Given 5 random test
sentences, it also increases the author classification accuracy by 3.43% on
average, compared with the N-gram methods using SRILM tools. An open source
implementation of our methodology is freely available at
https://github.com/zge/authorship-attribution/.
