Domain Specific Author Attribution Based on Feedforward Neural Network Language Models

TL;DR

This paper introduces a neural network-based language model for authorship attribution, demonstrating improved accuracy over traditional N-gram methods, especially with limited data and moderate author sets.

Contribution

It presents a novel neural network language model setup for authorship attribution and evaluates its performance, showing notable improvements over conventional methods.

Findings

NNLM reduces perplexity by 2.5%

Author classification accuracy increases by 3.43%

Performance is affected by text topic and data size

Abstract

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/.