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Mathscinet Classification Essay

Classification essays rank the groups of objects according to a common standard. For example, popular inventions may be classified according to their significance to the humankind.

Classification is a convenient method of arranging data and simplifying complex notions.

When you select a topic, do not forget about the length of your paper. Choose the topic you will be able to cover in your essay, do not write about something global or general.

Consider these examples:

  • Evaluate the best to worst methods of upbringing.
  • Rate the films according to their influence on people.
  • Classify careers according to the opportunities they offer.

You should point out the common classifying principle for the group you are writing about. It will become the thesis of your essay.

It is important for you to use clear method of classification in your essay, especially when you are dealing with subjective categories such as "quality" or "benefit". Make sure you explain what you mean by this term.

To organize a classification essay, the writer should:

  • categorize each group.
  • describe or define each category. List down the general characteristics and discuss them.
  • provide enough illustrative examples. An example should be a typical representative of the group.
  • point out similarities or differences of each category, using comparison-contrast techniques.
  • Dong, F., Zhang, Y.: Automatic features for essay scoring - an empirical study. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1072–1077 (2016)Google Scholar

  • Faulkner, A.R.: Automated Classification of Argument Stance in Student Essays: A Linguistically Motivated Approach with an Application for Supporting Argument Summarization. Ph.D. thesis, Graduate Center, City University of New York (2014)Google Scholar

  • Ghosh, D., Khanam, A., Han, Y., Muresan, S.: Coarse-grained argumentation features for scoring persuasive essays. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Short Papers, vol. 2, pp. 549–554. Association for Computational Linguistics (2016).

  • Habernal, I., Gurevych, I.: Argumentation mining in user-generated web discourse. Comput. Linguist. 43(1), 125–179 (2017)MathSciNetCrossRefGoogle Scholar

  • Houy, C., Niesen, T., Fettke, P., Loos, P.: Towards automated identification and analysis of argumentation structures in the decision corpus of the German Federal Constitutional Court. In: The 7th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST-2013) (2013)Google Scholar

  • Kluge, R.: Searching for Arguments - Automatic analysis of arguments about controversial educational topics in web documents. AV Akademikerverlaug, Saarbrücken (2014)Google Scholar

  • Landis, R.J., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)CrossRefMATHGoogle Scholar

  • Meyer, C.M., Mieskes, M., Stab, C., Gurevych, I.: DKPro Agreement: an open-source Java library for measuring inter-rater agreement. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland, pp. 105–109 (2014)Google Scholar

  • Peldszus, A., Stede, M.: Ranking the annotators: an agreement study on argumentation structure. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 196–204 (2013)Google Scholar

  • Stab, C., Gurevych, I.: Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 46–56. Association for Computational Linguistics (2014).

  • Wachsmuth, H., Al-Khatib, K., Stein, B.: Using argument mining to assess the argumentation quality of essays. In: Proceedings of the 26th International Conference on Computational Linguistics (COLING 2016), pp. 1680–1692, December 2016Google Scholar