Politicize and Depoliticize: A Study of Semantic Shifts on People’s Daily Fifty Years’ Corpus via Distributed Word Representation Space

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9332)


The semantic meanings of words are always changing with time. In this paper, we focus on semantic shifts, a certain type of semantic change, which indicates the meaning changes of a bunch of words that are influenced by social trends in specific time period. By training distributed word representation spaces for segmented time periods in diachronic corpus and mapping them into a universal semantic space, the semantic shifts of a certain cluster of words can be reflected as an offset vector in the universal space.

Further study shows that this semantic shift vector can be used as standard pattern to trace the words which have the similar semantic shift between other time periods.


Semantics Diachronic semantic change Word embedding space 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baker, P.: Times may change, but we will always have money: Diachronic variation in recent british english. Journal of English Linguistics 39(1), 65–88 (2011)CrossRefGoogle Scholar
  2. 2.
    Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. The Journal of Machine Learning Research 3, 1137–1155 (2003)zbMATHGoogle Scholar
  3. 3.
    Fisher, W.D.: On grouping for maximum homogeneity. Journal of the American Statistical Association 53(284), 789–798 (1958)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Gabrielatos, C., McEnery, T., Diggle, P.J., Baker, P.: The peaks and troughs of corpus-based contextual analysis. International Journal of Corpus Linguistics 17(2), 151–175 (2012)CrossRefGoogle Scholar
  5. 5.
    He, S., Zou, X., Xiao, L., Hu, J.: Construction of diachronic ontologies from people’s daily of fifty years. In: Language Resources and Evaluation Conference, pp. 3258–3263 (2014)Google Scholar
  6. 6.
    Kruszewski, G., Baroni, M.: Dead parrots make bad pets: exploring modifier effects in noun phrases. In: Lexical and Computational Semantics (* SEM 2014), p. 171 (2014)Google Scholar
  7. 7.
    Kulkarni, V., Al-Rfou, R., Perozzi, B., Skiena, S.: Statistically significant detection of linguistic change. arXiv preprint arXiv:1411.3315 (2014)
  8. 8.
    Michel, J.B., Shen, Y.K., Aiden, A.P., Veres, A., Gray, M.K., Pickett, J.P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., et al.: Quantitative analysis of culture using millions of digitized books. Science 331(6014), 176–182 (2011)CrossRefGoogle Scholar
  9. 9.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  10. 10.
    Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013)
  11. 11.
    Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: HLT-NAACL, pp. 746–751 (2013)Google Scholar
  12. 12.
    Rohrdantz, C., Hautli, A., Mayer, T., Butt, M., Keim, D.A., Plank, F.: Towards tracking semantic change by visual analytics. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers. Association for Computational Linguistics, vol. 2, pp. 305–310 (2011)Google Scholar
  13. 13.
    Sun, N., Chen, T., Xiao, L., Hu, J.: Diachronic deviation features in continuous space word representations. In: Sun, M., Liu, Y., Zhao, J. (eds.) NLP-NABD 2014 and CCL 2014. LNCS, vol. 8801, pp. 23–33. Springer, Heidelberg (2014) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  2. 2.Key Laboratory of Computational Linguistics, Ministry of EducationPeking UniversityBeijingChina

Personalised recommendations