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2017 | OriginalPaper | Chapter

Using Time Series Analysis for Estimating the Time Stamp of a Text

Authors : Costin-Gabriel Chiru, Madalina Toia

Published in: Advances in Time Series Analysis and Forecasting

Publisher: Springer International Publishing

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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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previous chapter Acanthuridae and Scarinae: Drivers of the Resilience of a Polynesian Coral Reef
next chapter Using LDA and Time Series Analysis for Timestamping Documents
Literature
1.
Jurafsky, D., Martin, J.: Speech and Language Processing. Prentice Hall (2000)
2.
Michel, J.-B., Shen, Y.K., Aiden, A.P., Veres, A., Gray, M.K., The Google Books Team, Pickett, J.P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M.A., Aiden, E.L.: Quantitative analysis of culture using millions of digitized books. Science 331(6014), 176–182 (2011)CrossRef
3.
Fromkin, V., Robert, R., Hyams, N.: An Introduction to Language, 7th edn. Thomson Wadswor (2003)
4.
Wijaya, D.T., Yeniterzi, R.: Understanding semantic change of words over centuries. In: DETECT’11, pp. 35–40 (2011)
5.
Mitra, S., Mitra, R., Riedl, M., Biemann, C., Mukherjee, A., Goyal, P.: That’s sick dude!: automatic identification of word sense change across different timescales. In: 52nd ACL, pp. 1020–1029 (2014)
6.
Petersen, A.M., Tenenbaum, J., Havlin, S., Stanley, H.E.: Statistical laws governing fluctuations in word use from word birth to word death. Sci. Rep. 2, 313 (2012)CrossRef
7.
Garcia-Fernandez, A., Ligozat, A.-L., Dinarelli, M., Bernhard, D.: When was it written? Automatically determining publication dates. In: String Processing and Information Retrieval, pp. 221–236 (2011)
8.
de Jong, F., Rode, H., Hiemstra, D.: Temporal language models for the disclosure of historical text. In: Proceedings of the AHC’05, pp. 161–168 (2005)
9.
Szymanski, T., Lynch, G.: UCD: diachronic text classification with character, word, and syntactic N-grams. In: SemEval 2015, 879–883 (2015)
10.
11.
Rubner, Y., Tomasi, C., Guibas, L. J.: A metric for distributions with applications to image databases. In: Computer Vision and Image Understanding, pp. 86–109 (2004)
12.
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2001)MATH
Metadata
Title
Using Time Series Analysis for Estimating the Time Stamp of a Text
Authors
Costin-Gabriel Chiru
Madalina Toia
Copyright Year
2017
Book
Advances in Time Series Analysis and Forecasting

Print ISBN: 978-3-319-55788-5

Electronic ISBN: 978-3-319-55789-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-55789-2_3