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Retrieve relevant informations in response to the information requests.
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=== indexing
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==== indexing
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'organize data in such a way that it can be easily retrieved later on'(<<Ignatow_etal2017>>,137)
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=== searching/querying
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==== searching/querying
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'take information requests in the form of queries and return relevant documents'(<<Ignatow_etal2017>>,137). There are different models in order to estimate the similarity between records and the search queries (e.g. boolean, vector space or a probabilistic model)(ibid).
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... | ... | @@ -169,7 +169,7 @@ Inclusion of meta-data. Refer especially <<roberts2013>>. |
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===== automated narrative, argumentative structures, irony, metaphor detection/extraction
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For automated narrative methapor analysis see (<<Ignatow_etal2017>>, 89-106. For argumentative structures(Task: Retrieving sentential arguments for any given controversial topic) <<Stab_etal2018>> .Refer for a current overview <<Cabrio2018>>.
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==== network analysis/modelling
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===
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== social complexity modeling/ social simulation
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... | ... | @@ -275,6 +271,8 @@ Using methods to predict the future for estimation of current values. (Example: |
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[bibliography]
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== References
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- [[[Cabrio2018]]] Cabrio, E., & Villata, S. (2018). Five years of argument mining: a data-driven analysis. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 5427–5433).
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- [[[Han_etal2012]]] Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Saint Louis, UNITED STATES: Elsevier Science & Technology.
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- [[[Ignatow_etal2017]]] Ignatow, G., & Mihalcea, R. F. (2017). Text mining: A guidebook for the social sciences. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.
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... | ... | @@ -291,4 +289,6 @@ Using methods to predict the future for estimation of current values. (Example: |
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- [[[Salganik2018]]] Salganik, M. J. (2018). Bit by bit: Social research in the digital age.
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- [[[Stab_etal2018]]] Stab, C., Daxenberger, J., Stahlhut, C., Miller, T., Schiller, B., Tauchmann, C., . . . Gurevych, I. (2018). ArgumenText: Searching for Arguments in Heterogeneous Sources. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations (pp. 21–25).
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- [[[Wickham_etal2017]]] Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, tidy, transform, visualize, and model data. Beijing, Boston, Farnham, Sebastopol, Tokyo: O’Reilly UK Ltd. |