Cover not available

In:Language and Text: Data, models, information and applications
Edited by Adam Pawłowski, Jan Mačutek, Sheila Embleton and George Mikros
[Current Issues in Linguistic Theory 356] 2021
► pp. 257–270

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload Book PDF
EPUB logo with download linkDownload Book EPUB
authorization required

Analysis of English text genre classification based on dependency types

 | Guangdong University of Foreign Studies
Published online: 22 December 2021
DOI logo with link
https://doi.org/10.1075/cilt.356.17wan
Abstract
The present study aims to explore whether dependency type can be used as a distinctive text vector for classifying English genres. Three classification methods, namely principal component analysis, hierarchical clustering, and random forest were employed to investigate the clustering effect. Results show that dependency type is an effective measure in distinguishing text genres, especially between spoken genre and written genre.
Article outline
  • 1.Introduction
  • 2.Treebank establishment
  • 3.Methods
    • 3.1Principal component analysis
    • 3.2Hierarchical cluster analysis
    • 3.3Random Forest
  • 4.Results and discussion
    • 4.1PCA
    • 4.2Text clustering
    • 4.3Random forest
  • 5.Conclusions
  • Acknowledgements
  • Notes
  • References
  • Appendix
References (34)
References
Baayen, Harald, Hans Van Halteren & Fiona Tweedie. 1996. Outside the cave of shadows: Using syntactic annotation to enhance authorship attribution. Literary and Linguistic Computing 11(3). 121–132. Google Scholar logo with link to Google Scholar
Baayen, R. Harald. 2008. Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
Baharudin, Baharum, Lam H. Lee, Khairullah Khan & Aurangzeb Khan. 2010. A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology 1(1). 4–20. Google Scholar logo with link to Google Scholar
Biber, Douglas. 1993. Using register-diversified corpora for general language studies. Computational Linguistics 19(2). 219–241.Google Scholar logo with link to Google Scholar
. 1995. Dimensions of register variation: A cross-linguistic comparison. Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
Breiman, Lee. 2001. Random forests. Machine Learning 45(1). 5–32. Google Scholar logo with link to Google Scholar
Burnard, Lou. 2000. Reference guide for the British National Corpus (World Edition). Oxford: Oxford University Computing Services.Google Scholar logo with link to Google Scholar
de Marneffe, Marie-Catherine & Christopher D. Manning. 2008. Stanford typed dependencies manual. Technical report, Stanford University. [URL]
Eppler, Eva M. 2005. The syntax of German-English code-switching. London: University of London dissertation.Google Scholar logo with link to Google Scholar
Feldman, Sergey, M. A. Marin, Mari Ostendorf & Maya R. Gupta. 2009. Part-of-speech histograms for genre classification of text. In 2009 IEEE International Conference Acoustics, Speech and Signal Processing, 4781–4784). Taipei: IEEE. Google Scholar logo with link to Google Scholar
Futrell, Richard, Kyle Mahowald & Edward Gibson. 2015. Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences 112(33). 10336–10341. Google Scholar logo with link to Google Scholar
Gao, Song & Zhiwei Feng. 2011. Research on text clustering based on dependency treebank. Journal of Chinese Information Processing 25(3). 59–63.Google Scholar logo with link to Google Scholar
Hiranuma, So. 1999. Syntactic difficulty in English and Japanese: A textual study. UCL Working Papers in Linguistics 11. 309–322.Google Scholar logo with link to Google Scholar
Hollingsworth, Charles. 2012. Using dependency-based annotations for authorship identification. Text, Speech and Dialogue 7499. 314–319. Google Scholar logo with link to Google Scholar
Hotelling, Harold. 1933. Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24(6). 417–441. Google Scholar logo with link to Google Scholar
Hou, Renkui & Minghu Jiang. 2014. Analysis on Chinese quantitative stylistic features based on text mining. Digital Scholarship in the Humanities 31 (2). 357–367. Google Scholar logo with link to Google Scholar
Hou, Renkui, Jiang Yang & Minghu Jiang. 2014. A study on Chinese quantitative stylistic features and relation among different styles based on text clustering. Journal of Quantitative Linguistics 21(3). 246–280. Google Scholar logo with link to Google Scholar
Hudson, Richard A. 1990. English word grammar. Oxford: Basil Blackwell.Google Scholar logo with link to Google Scholar
Jiang, Jingyang & Haitao Liu. 2015. The effects of sentence length on dependency distance, dependency direction and the implications–Based on a parallel English–Chinese dependency treebank. Language Sciences 50. 93–104. Google Scholar logo with link to Google Scholar
Kessler, Brett, Geoffrey Nunberg & Hinrich Schütze. 1997. Automatic detection of text genre. In Philip R. Cohen & Wolfgang Wahlster (ed.), Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, 32–38. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar logo with link to Google Scholar
Liu, Haitao. 2008. Dependency distance as a metric of language comprehension difficulty. Journal of Cognitive Science 9(2). 159–191. Google Scholar logo with link to Google Scholar
. 2010. Dependency direction as a means of word-order typology: A method based on dependency treebanks. Lingua 120(6). 1567–1578. Google Scholar logo with link to Google Scholar
Liu, Haitao, Richard Hudson & Zhiwei Feng. 2009a. Using a Chinese treebank to measure dependency distance. Corpus Linguistics and Linguistic Theory 5(2). 161–174. Google Scholar logo with link to Google Scholar
Liu, Haitao, Yiyi Zhao & Wenwen Li. 2009b. Chinese syntactic and typological properties based on dependency syntactic treebanks. Poznań Studies in Contemporary Linguistics 45(4). 509–523. Google Scholar logo with link to Google Scholar
Liu, Haitao, Chunshan Xu & Junying Liang. 2017. Dependency distance: A new perspective on syntactic patterns in natural languages. Physics of Life Reviews 21. 171–193. Google Scholar logo with link to Google Scholar
Nivre, Joakim, Hall Johan, Kübler Sandra, McDonald Ryan, Nilsson Jens, Riedel Sebastian & Yuret Deniz. 2007. The CoNLL 2007 shared task on dependency parsing. In Jason Eisner (ed.), Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (915–932). Prague: Association for Computational Linguistics.Google Scholar logo with link to Google Scholar
Nivre, Joakim, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajič, Christopher D. Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, Reut Tsarfaty & Daniel Zeman. 2016. Universal Dependencies v1: A multilingual treebank collection. In Nicoletta Calzolari et al. (eds.), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 1659–1666. Portorož: European Language Resources Association (ELRA).Google Scholar logo with link to Google Scholar
Rygl, Jan. 2014. Automatic adaptation of author’s stylometric features to document types. In International Conference on Text, Speech, and Dialogue, 53–61. Cham: Springer. Google Scholar logo with link to Google Scholar
Stamatatos, Efstathios, Nikos Fakotakis & George Kokkinakis. 2000a. Automatic text categorization in terms of genre and author. Computational Linguistics 26(4). 471–495. Google Scholar logo with link to Google Scholar
. 2000b. Text genre detection using common word frequencies. In Martin Kay (ed.) Proceedings of the 18th conference on Computational Linguistics, Volume 2, 808–814. Stroudsburg, PA: Association for Computational Linguistics. Google Scholar logo with link to Google Scholar
Tesnière, Lucien. 1959. Eléments de syntaxe structurale. Paris: Librairie C. Klincksieck.Google Scholar logo with link to Google Scholar
Wang, Yaqin & Haitao Liu. 2017. The effects of genre on dependency distance and dependency direction. Language Sciences 59. 135–147. Google Scholar logo with link to Google Scholar
Zaghloul, Waleed, Sang M. Lee & Silvana Trimi. 2013. Text classification: Neural networks vs support vector machines. Industrial Management and Data Systems 109(5). 708–717. Google Scholar logo with link to Google Scholar
Zhang, Wen, Xijin Tang & Toshida Yoshida. 2015. TESC: An approach to TExt classification using Semi-supervised Clustering. Knowledge Based Systems, 152–160. Google Scholar logo with link to Google Scholar
Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue