Skip to main content
SpringerLink
Log in
Book cover

Intelligent Data Engineering and Analytics pp 171–181Cite as

Significance of Network Properties of Function Words in Author Attribution

  • Conference paper
  • First Online:

  • 635 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

Author identification or attribution helps in identifying the author of unknown texts and is used in plagiarism detection, identification of writers of threatening documents and resolving disputed authorship of historical documents. Stylometry and machine learning are the most popular approaches to this problem where statistical methods are employed to extract signatures of authors of known texts and these features are used to predict authorship of unknown documents. Complex network approach to feature extraction has focused on content words ignoring function words as noise. In this paper, features of function words of texts are extracted from the word co-occurrence network of texts and used for classification. The results of these experiments are found to have high accuracy. The results of the experiments using function words and content words are compared.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Project Gutenberg, www.gutenberg.org/ebooks/.

  2. 2.

    IMDB 62, www.imdb.com.

  3. 3.

    Zhi Liu: Reuters C50, https://archive.ics.uci.edu/ml/datasets/Reuter 50 50.

References

  1. Todorov, T., Howard, R.: Poetics of Prose. Cornell Press, New York (1977)

    Google Scholar 

  2. Tomori, S., Milne, J., Banjo, A., Afloyan, A.: The Morphology of Present-Day English: An Introduction. Heinemann Educational, London (1977)

    Google Scholar 

  3. Allan, B., Trembly, S. (eds.): The Fontana Dictionary of Modern Thoughts. Fontana, London

    Google Scholar 

  4. Westerhout, E.: Definition extraction using linguistic and structural features. In: Proceedings of the 1st Workshop on Definition Extraction 61–67 (2009)

    Google Scholar 

  5. Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Computer-based authorship attribution without lexical measures. Lang. Resour. Eval. 35, 193–214 (2001). https://doi.org/10.1023/A:1002681919510

    Article  Google Scholar 

  6. Stamatatos, E.: A survey of modern authorship methods. https://doi.org/10.1080/00335634309380866

  7. Kestemont, M.: Function words in authorship attribution. From Black Magic to Theory? 59–66 (2015). https://doi.org/10.3115/v1/w14-0908

  8. Dang, T.N.Y., Webb, S.: Making an essential word list for beginners. In: Making and Using Word Lists for Language Learning and Testing, pp. 153–167. John Benjamins, Amsterdam (2016). https://doi.org/10.1075/z.208.15ch15

  9. Estrada, E.: The structure of complex networks: theory and applications. Published to Oxford Scholarship Online (2013). https://doi.org/10.1093/acprof:oso/9780199591756.001.0001

  10. Barabasi, A.-L.: Linked: how everything is connected to everything else and what it means. Plume (2003)

    Google Scholar 

  11. Cong, J., Liu, H.: Approaching human language with complex networks (2014). https://doi.org/10.1016/j.plrev.2014.04.004

  12. Matsuo, Y., Ishizuka, M.: Flairs02. Dvi. 1–5 (2003)

    Google Scholar 

  13. Silva, T.C., Amancio, D.R.: Word sense disambiguation via high order of learning in complex networks. Epl. 98 (2012). https://doi.org/10.1209/0295-5075/98/58001

  14. Amancio, D.R., Oliveira, O.N., Costa, L.D.F.: Unveiling the relationship between complex networks metrics and word senses. Epl. 98 (2012). https://doi.org/10.1209/0295-5075/98/18002

  15. Pardo, T.A.S., Antiqueira, L., Nunes, M.D.G.V., Oliveira, O.N., Da Fontoura Costa, L.: Using complex networks for language processing: the case of summary evaluation. In: Proceedings of 2006 International Conference Communication Circuits System ICCCAS, vol 4, pp 2678–2682 (2006). https://doi.org/10.1109/ICCCAS.2006.285222

  16. Aaronson, S., Aaronson, S.: Ask me anything. Quantum Comput. Since Democritus. 48, 343–362 (2013). https://doi.org/10.1017/cbo9780511979309.023

    Article  Google Scholar 

  17. Liu, J., Wang, J.: Keyword e e xthren manyicularey, as keywofdomen semantic. 129–134

    Google Scholar 

  18. Amancio, D.R.: A complex network approach to stylometry. PLoS ONE 10, 1–21 (2015). https://doi.org/10.1371/journal.pone.0136076

    Article  Google Scholar 

  19. Amancio, D.R., Altmann, E.G., Oliveira, O.N., Da Fontoura Costa, L.: Comparing intermittency and network measurements of words and their dependence on authorship. New J. Phys. 13 (2011). https://doi.org/10.1088/1367-2630/13/12/123024

  20. Yule, G.U.: On sentence-length as a statistical characteristic of style in prose: with application to two cases of disputed authorship. Biometrika 30, 363 (1939). https://doi.org/10.2307/2332655

    Article  Google Scholar 

  21. Zipf, G.K.: Selected studies of the principle of relative frequency in language. Harvard University Press, Cambridge, MA (1932)

    Google Scholar 

  22. Mosteller, F., Wallace, D.: Inference in an authorship problem. J. Am. Stat. Assoc. 58, 275–309 (1963). https://doi.org/10.2307/2283270, https://www.jstor.org/stable/2283270

  23. Gorman, R.: Author identification of short texts using dependency treebanks without vocabulary 1–14 (2019)

    Google Scholar 

  24. NagaPrasad, S., Narsimha, V.B., Vijayapal Reddy, P., Vinaya Babu, A.: Influence of lexical, syntactic and structural features and their combination on authorship attribution for TeluguTex. Procedia Comput. Sci. 48, 58–64(2015). https://doi.org/10.1016/j.procs.2015.04.110

  25. Zhang, C., Wu, X., Niu, Z., Ding, W.: Authorship identification from unstructured texts. Knowledge-Based Syst. 66, 99–111 (2014). https://doi.org/10.1016/j.knosys.2014.04.025

    Article  Google Scholar 

  26. Adhikari, A., Subramaniyan, S.: Author identification: using text mining. Feat Eng Net Emb. SemanticScholar.Org. (2016)

    Google Scholar 

  27. Rexha, A., Kröll, M., Ziak, H., Kern, R.: Authorship identification of documents with high content similarity. Scientometrics 115, 223–237 (2018). https://doi.org/10.1007/s11192-018-2661-6

    Article  Google Scholar 

  28. Lahiri, S., Mihalcea, R.: Authorship attribution using word network features (2013)

    Google Scholar 

  29. Menon, R.K., Choi, Y.: Domain independent authorship attribution without domain adaptation (2011)

    Google Scholar 

  30. Akimushkin, C., Amancio, D.R., Oliveira, O.N.: On the role of words in the network structure of texts: application to authorship attribution. Phys. A Stat. Mech. Appl. 495 (2018). https://doi.org/10.1016/j.physa.2017.12.054

  31. Akimushkin, C., Amancio, D.R., Oliveira, O.N.: Text authorship identified using the dynamics of word co-occurrence networks. PLoS One 12 (2017). https://doi.org/10.1371/journal.pone.0170527

  32. Marinho, V.Q., de Arruda, H.F., Sinelli, T., Costa, L. da F., Amancio, D.R.: On the “calligraphy” of books. In: Proceedings of TextGraphs-11: The Workshop on Graph-Based Methods for Natural Language Processing (2017). https://doi.org/10.18653/v1/W17-2401

  33. Marinho, V.Q., Hirst, G., Amancio, D.R.: Authorship attribution via network motifs identification. In: Proceedings—2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016 (2017). https://doi.org/10.1109/BRACIS.2016.071

  34. Marinho, V.Q., Hirst, G., Amancio, D.R.: Labelled network subgraphs reveal stylistic subtleties in written texts. J. Complex Net. 6, 620–638 (2018). https://doi.org/10.1093/COMNET/CNX047

    Article  MathSciNet  Google Scholar 

  35. Macke, S., Hirshman, J.: Deep sentence-level authorship attribution. CS224N Proj. 1–7 (2015). https://doi.org/10.1016/j.jpcs.2013.01.035

  36. Yao, L., Liu, D.: Wallace: Author detection via recurrent neural networks. CS224N Proj. 1–7 (2015)

    Google Scholar 

  37. Wright, D.: Using word n-grams to identify authors and idiolects. Int. J. Corpus Linguist. 22, 212–241 (2017). https://doi.org/10.1075/ijcl.22.2.03wri

    Article  Google Scholar 

  38. Panicheva, P., Cardiff, J., Rosso, P.: Personal sense and idiolect: Combining authorship attribution and opinion analysis. In: Proceedings of 7th International Conference on Language Resources and Evaluation LR 134–1137 (2010)

    Google Scholar 

  39. Kocher, M., Savoy, J.: Distance measures in author profiling. Inf. Process. Manag. 53, 1103–1119 (2017). https://doi.org/10.1016/j.ipm.2017.04.004

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cochin University of Science & Technology, Kochi, Kerala, India

    Sariga Raj, B. Kannan & V. P. Jagathy Raj

Authors
  1. Sariga Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Kannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. P. Jagathy Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sariga Raj .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Raj, S., Kannan, B., Jagathy Raj, V.P. (2021). Significance of Network Properties of Function Words in Author Attribution. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_17

Download citation

Publish with us

Policies and ethics

Search

Navigation