Abstract
Language is our main communication tool. Deep understanding of its evolution is imperative for many related research areas including history, humanities, social sciences, etc. To this end, we are interested in the task of segmenting long-term document archives into naturally coherent periods based on the evolving word semantics. There are many benefits of such segmentation such as better representation of content in long-term document collections, and support for modeling and understanding semantic drift. We propose a two-step framework for learning time-aware word semantics and periodizing document archive. Encouraging effectiveness of our model is demonstrated on the New York Times corpus spanning from 1990 to 2016.
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Notes
- 1.
The overall vocabulary V is the union of vocabularies of each time unit, and thus it is possible for some \(w \in V\) to not appear at all in some time units. This includes emerging words and dying words that are typical in real-world news corpora.
- 2.
These sections are Arts, Business, Fashion & Style, Health, Home & Garden, Real Estate, Science, Sports, Technology, U.S., World.
References
Alemi, A.A., Ginsparg, P.: Text segmentation based on semantic word embeddings. arXiv preprint arXiv:1503.05543 (2015)
Beeferman, D., Berger, A., Lafferty, J.: Statistical models for text segmentation. Mach. Learn. 34(1–3), 177–210 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Campos, R., Dias, G., Jorge, A.M., Jatowt, A.: Survey of temporal information retrieval and related applications. ACM Comput. Surv. (CSUR) 47(2), 1–41 (2014)
Choi, F.Y.: Advances in domain independent linear text segmentation. arXiv preprint cs/0003083 (2000)
Choi, F.Y., Wiemer-Hastings, P., Moore, J.D.: Latent semantic analysis for text segmentation. In: Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (2001)
Degaetano-Ortlieb, S., Teich, E.: Using relative entropy for detection and analysis of periods of diachronic linguistic change. In: Proceedings of the Second Joint SIGHUM Workshop, pp. 22–33 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL 2019, pp. 4171–4186 (2019)
Du, L., Buntine, W., Johnson, M.: Topic segmentation with a structured topic model. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 190–200 (2013)
Firth, J.R.: Papers in Linguistics 1934–1951: Repr. Oxford University Press (1961)
Fragkou, P., Petridis, V., Kehagias, A.: A dynamic programming algorithm for linear text segmentation. J. Intell. Inf. Syst. 23(2), 179–197 (2004)
Gries, S.T., Hilpert, M.: Variability-based neighbor clustering: a bottom-up approach to periodization in historical linguistics (2012)
Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, pp. 1489–1501. Association for Computational Linguistics, August 2016. https://doi.org/10.18653/v1/P16-1141. https://www.aclweb.org/anthology/P16-1141
Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Comput. Linguist. 23(1), 33–64 (1997)
Kulkarni, V., Al-Rfou, R., Perozzi, B., Skiena, S.: Statistically significant detection of linguistic change. In: Proceedings of the 24th International Conference on World Wide Web, pp. 625–635 (2015)
Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, pp. 2177–2185 (2014)
Lieberman, E., Michel, J.B., Jackson, J., Tang, T., Nowak, M.A.: Quantifying the evolutionary dynamics of language. Nature 449(7163), 713 (2007)
Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behav. Res. Methods Instrum. Comput. 28(2), 203–208 (1996)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
Pagel, M., Atkinson, Q.D., Meade, A.: Frequency of word-use predicts rates of lexical evolution throughout Indo-European history. Nature 449(7163), 717 (2007)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Pevzner, L., Hearst, M.A.: A critique and improvement of an evaluation metric for text segmentation. Comput. Linguist. 19–36 (2002)
Riedl, M., Biemann, C.: Text segmentation with topic models. J. Lang. Technol. Comput. Linguist. 47–69 (2012)
Schätzle, C., Booth, H.: DiaHClust: an iterative hierarchical clustering approach for identifying stages in language change. In: Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, pp. 126–135 (2019)
Sehikh, I., Fohr, D., Illina, I.: Topic segmentation in ASR transcripts using bidirectional RNNs for change detection. In: 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 512–518. IEEE (2017)
Tahmasebi, N., Borin, L., Jatowt, A.: Survey of computational approaches to diachronic conceptual change. arXiv preprint arXiv:1811.06278 (2018)
Tseng, P.: Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl. 109(3), 475–494 (2001)
Utiyama, M., Isahara, H.: A statistical model for domain-independent text segmentation. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, pp. 499–506 (2001)
Yao, Z., Sun, Y., Ding, W., Rao, N., Xiong, H.: Dynamic word embeddings for evolving semantic discovery. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 673–681 (2018)
Zhang, Y., Jatowt, A., Bhowmick, S., Tanaka, K.: Omnia Mutantur, Nihil Interit: connecting past with present by finding corresponding terms across time. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 645–655 (2015)
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This paper is based on results obtained from a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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Duan, Y., Jatowt, A., Yoshikawa, M., Liu, X., Matono, A. (2021). Diachronic Linguistic Periodization of Temporal Document Collections for Discovering Evolutionary Word Semantics. In: Ke, HR., Lee, C.S., Sugiyama, K. (eds) Towards Open and Trustworthy Digital Societies. ICADL 2021. Lecture Notes in Computer Science(), vol 13133. Springer, Cham. https://doi.org/10.1007/978-3-030-91669-5_1
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