Abstract
Language is constantly changing, with words being created or disappearing over time. Moreover, the usage of different words tends to fluctuate due to influences from different fields, such as historical events, cultural movements or scientific discoveries. These changes are reflected in the written texts and thus, by tracking them, one can determine the moment when these texts were written. In this paper, we present an application based on time series analysis built on top of the Google Books N-gram corpus to determine the time stamp of different written texts. The application is using two heuristics: words’ fingerprinting, to find the time interval when they were most probable used, and words’ importance for the given text, to weight the influence of words’ fingerprinting for estimating the text time stamp. Combining these two heuristics allows time stamping of that text.
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Acknowledgements
This work has been funded by University Politehnica of Bucharest, through the “Excellence Research Grants” Program, UPB–GEX. Identifier: UPB–EXCELENȚĂ–2016 Aplicarea metodelor de învățare automată în analiza seriilor de timp (Applying machine learning techniques in time series analysis), Contract number 09/26.09.2016.
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Chiru, CG., Toia, M. (2017). Using Time Series Analysis for Estimating the Time Stamp of a Text. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Advances in Time Series Analysis and Forecasting. ITISE 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55789-2_3
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DOI: https://doi.org/10.1007/978-3-319-55789-2_3
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