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Semantic word shifts in a scientific domain

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Abstract

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.

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Acknowledgements

This work is funded by the National Natural Science Foundation of China (Grant Nos. 71420107026 and 71704138). The present study is an extended version of an article presented at the 16th International Conference on Scientometrics and Informetrics, Wuhan (China), 16–20 October 2017 (Chen et al. 2017a).

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Authors and Affiliations

  1. Shanghai University, Shanghai, China

    Baitong Chen

  2. Indiana University, Bloomington, USA

    Ying Ding

  3. Wuhan University, Wuhan, China

    Ying Ding & Feicheng Ma

  4. Tianjin Normal University, Tianjin, China

    Ying Ding

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  1. Baitong Chen
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  2. Ying Ding
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  3. Feicheng Ma
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Correspondence to Baitong Chen.

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Chen, B., Ding, Y. & Ma, F. Semantic word shifts in a scientific domain. Scientometrics 117, 211–226 (2018). https://doi.org/10.1007/s11192-018-2843-2

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