Domain Specific Author Attribution based on Feedforward Neural Network Language Models

Zhenhao Ge, Yufang Sun

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/.

References

  1. Bengio, Y., Ducharme, R., Vincent, P., and Janvin, C. (2003). A neural probabilistic language model. The Journal of Machine Learning Research, 3:1137-1155.
  2. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
  3. Coyotl-Morales, R. M., Villase n˜or-Pineda, L., Montes-y Gómez, M., and Rosso, P. (2006). Authorship attribution using word sequences. In Progress in Pattern Recognition, Image Analysis and Applications, pages 844-853. Springer.
  4. Ebrahimpour, M., Putni n¸s?, T. J., Berryman, M. J., Allison, A., Ng, B. W.-H., and Abbott, D. (2013). Automated authorship attribution using advanced signal classification techniques. PloS one, 8(2):e54998.
  5. Ge, Z. and Sun, Y. (2015). Sleep stages classification using neural networks with multi-channel neural data. In Brain Informatics and Health, pages 306-316. Springer.
  6. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.- r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6):82-97.
  7. Juola, P. (2006). Authorship attribution. Foundations and Trends in information Retrieval, 1(3):233-334.
  8. Kes?elj, V., Peng, F., Cercone, N., and Thomas, C. (2003). N-gram-based author profiles for authorship attribution. In Proceedings of the conference pacific association for computational linguistics, PACLING, volume 3, pages 255-264.
  9. Koppel, M., Schler, J., and Argamon, S. (2009). Computational methods in authorship attribution. Journal of the American Society for information Science and Technology, 60(1):9-26.
  10. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105.
  11. Luyckx, K. (2011). Scalability issues in authorship attribution. ASP/VUBPRESS/UPA.
  12. Luyckx, K. and Daelemans, W. (2008). Authorship attribution and verification with many authors and limited data. In Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pages 513-520. Association for Computational Linguistics.
  13. Mikolov, T., Karafiát, M., Burget, L., CernockÈ, J., and Khudanpur, S. (2010). Recurrent neural network based language model. In INTERSPEECH 2010, Makuhari, Chiba, Japan, September 26-30, 2010, pages 1045-1048.
  14. Mnih, A. (2010). Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering. PhD thesis, University of Toronto.
  15. Mnih, A. and Hinton, G. (2007). Three new graphical models for statistical language modelling. In Proceedings of the 24th international conference on Machine learning, pages 641-648. ACM.
  16. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3):130-137.
  17. Seroussi, Y., Zukerman, I., and Bohnert, F. (2011). Authorship attribution with latent dirichlet allocation. In Proceedings of the fifteenth conference on computational natural language learning, pages 181-189. Association for Computational Linguistics.
  18. Socher, R., Lin, C. C., Manning, C., and Ng, A. Y. (2011). Parsing natural scenes and natural language with recursive neural networks. In Proceedings of the 28th international conference on machine learning (ICML11), pages 129-136.
  19. Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for information Science and Technology, 60(3):538-556.
  20. Stolcke, A. et al. (2002). Srilm-an extensible language modeling toolkit. In INTERSPEECH.
Download


Paper Citation


in Harvard Style

Ge Z. and Sun Y. (2016). Domain Specific Author Attribution based on Feedforward Neural Network Language Models . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 597-604. DOI: 10.5220/0005710005970604


in Bibtex Style

@conference{icpram16,
author={Zhenhao Ge and Yufang Sun},
title={Domain Specific Author Attribution based on Feedforward Neural Network Language Models},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={597-604},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005710005970604},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Domain Specific Author Attribution based on Feedforward Neural Network Language Models
SN - 978-989-758-173-1
AU - Ge Z.
AU - Sun Y.
PY - 2016
SP - 597
EP - 604
DO - 10.5220/0005710005970604