Abstract
This paper introduces a novel approach to the study of lexical and pragmatic meaning called ‘sociolexical profiling’, which aims at correlating the use of lexical items with author-attributed demographic features, such as gender, age, profession, and education. The approach was applied to a case study of a set of English idioms derived from the Pattern Dictionary of English Verbs (PDEV), a corpus-driven lexical resource which defines verb senses in terms of the phraseological patterns in which a verb typically occurs. For each selected idiom, a gender profile was generated based on data extracted from the Blog Authorship Corpus (BAC) in order to establish whether any statistically significant differences can be detected in the way men and women use idioms in every-day communication. A quantitative and qualitative analysis of the gender profiles was subsequently performed, enabling us to test the validity of the proposed approach. If performed on a large scale, we believe that sociolexical profiling will have important implications for several areas of research, including corpus lexicography, translation, creative writing, forensic linguistics, and natural language processing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This is not surprising; idioms are known to generally occur with very low frequencies in most corpora.
References
Argamon, S., Koppel, M., Schler, J., Pennebaker, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)
Baisa, V., El Maarouf, I., Rychlý, P., Rambousek, A.: Software and data for corpus pattern analysis. In: Horák, A. et al. (eds.) RASLAN, pp. 75–86. Tribun EU (2015)
Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining (2006). http://citeseer.ist.psu.edu/esuli06sentiwordnet.html
Grieve, J.: Quantitative authorship attribution: an evaluation of techniques. Lit. Linguist. Comput. 22(3), 251–270 (2007)
Hanks, P.: Corpus pattern analysis. In: Williams, G., Vessier, S. (eds.) 11th Euralex International Congress, Proceedings, pp. 87–97. Université de Bretagne-Sud, Lorient (2004)
Hanks, P.: Lexical Analysis: Norms and Exploitations. MIT Press, Cambridge (2013)
Ježek, E., Hanks, P.: What lexical sets tell us about conceptual categories. Lexis: E-J. Eng. lexicol. 7–22 (2010). 4: Corpus Linguistics and the Lexicon
Ježek, E., Magnini, B., Feltracco, A., Bianchini, A., Popescu, O.: T-PAS: a resource of corpus-derived typed predicate argument structures for linguistic analysis and semantic processing. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 890–895. ELRA (2014)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427–431 (2017)
Juola, P.: Author attribution. Found. Trends Inf. Retr. 1(3), 233–334 (2008)
Kilgarriff, A., et al.: The sketch engine: ten years on. Lexicography 1(1), 7–36 (2014)
Nazar, R., Renau, I.: Ontology population using corpus statistics. In: Papini, O. et al. (eds.) Proceedings of the Joint Ontology Workshops 2015 Co-located with the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015) (2015)
Leech, G.: 100 million words of English: the British National Corpus (BNC). Lang. Res. 28(1), 1–13 (1992)
Oakes, M.: Literary Detective Work on the Computer. John Benjamins, Amsterdam/Philadelphia (2014)
Renau, I., Nazar, R.: Verbario. http://www.verbario.com. Accessed 14 May 2019
Ruppenhofer, J., Ellsworth, M., Petruck, M.R., Johnson, C.R., Scheffczyk, J.: FrameNet II: Extended Theory and Practice. ICSI, Berkeley (2006)
Savoy, J.: Comparative evaluation of term selection functions for authorship attribution. Lit. Linguist. Comput. 30(2), 246–261 (2015)
Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3), 538–556 (2008)
Schler J., Koppel, M., Argamon, S., Pennebaker, J.: Effects of age and gender on blogging. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 199 –205. AAAI (2006)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Može, S., Mohamed, E. (2019). Profiling Idioms:. In: Corpas Pastor, G., Mitkov, R. (eds) Computational and Corpus-Based Phraseology. EUROPHRAS 2019. Lecture Notes in Computer Science(), vol 11755. Springer, Cham. https://doi.org/10.1007/978-3-030-30135-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-30135-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30134-7
Online ISBN: 978-3-030-30135-4
eBook Packages: Computer ScienceComputer Science (R0)