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used in plagiarism detection, identification of writers of threatening documents and resolving disputed authorship of historical documents.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 to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Project Gutenberg, www.gutenberg.org/ebooks/.
- 2.
IMDB 62, www.imdb.com.
- 3.
Zhi Liu: Reuters C50, https://archive.ics.uci.edu/ml/datasets/Reuter 50 50.
References
Todorov, T., Howard, R.: Poetics of Prose. Cornell Press, New York (1977)
Tomori, S., Milne, J., Banjo, A., Afloyan, A.: The Morphology of Present-Day English: An Introduction. Heinemann Educational, London (1977)
Allan, B., Trembly, S. (eds.): The Fontana Dictionary of Modern Thoughts. Fontana, London
Westerhout, E.: Definition extraction using linguistic and structural features. In: Proceedings of the 1st Workshop on Definition Extraction 61–67 (2009)
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
Stamatatos, E.: A survey of modern authorship methods. https://doi.org/10.1080/00335634309380866
Kestemont, M.: Function words in authorship attribution. From Black Magic to Theory? 59–66 (2015). https://doi.org/10.3115/v1/w14-0908
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
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
Barabasi, A.-L.: Linked: how everything is connected to everything else and what it means. Plume (2003)
Cong, J., Liu, H.: Approaching human language with complex networks (2014). https://doi.org/10.1016/j.plrev.2014.04.004
Matsuo, Y., Ishizuka, M.: Flairs02. Dvi. 1–5 (2003)
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
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
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
Aaronson, S., Aaronson, S.: Ask me anything. Quantum Comput. Since Democritus. 48, 343–362 (2013). https://doi.org/10.1017/cbo9780511979309.023
Liu, J., Wang, J.: Keyword e e xthren manyicularey, as keywofdomen semantic. 129–134
Amancio, D.R.: A complex network approach to stylometry. PLoS ONE 10, 1–21 (2015). https://doi.org/10.1371/journal.pone.0136076
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
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
Zipf, G.K.: Selected studies of the principle of relative frequency in language. Harvard University Press, Cambridge, MA (1932)
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
Gorman, R.: Author identification of short texts using dependency treebanks without vocabulary 1–14 (2019)
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
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
Adhikari, A., Subramaniyan, S.: Author identification: using text mining. Feat Eng Net Emb. SemanticScholar.Org. (2016)
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
Lahiri, S., Mihalcea, R.: Authorship attribution using word network features (2013)
Menon, R.K., Choi, Y.: Domain independent authorship attribution without domain adaptation (2011)
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
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
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
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
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
Macke, S., Hirshman, J.: Deep sentence-level authorship attribution. CS224N Proj. 1–7 (2015). https://doi.org/10.1016/j.jpcs.2013.01.035
Yao, L., Liu, D.: Wallace: Author detection via recurrent neural networks. CS224N Proj. 1–7 (2015)
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
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)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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
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
DOI: https://doi.org/10.1007/978-981-15-5679-1_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5678-4
Online ISBN: 978-981-15-5679-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)