Semantic word shifts in a scientific domain

Baitong Chen (), Ying Ding () and Feicheng Ma ()
Additional contact information
Baitong Chen: Shanghai University
Ying Ding: Indiana University
Feicheng Ma: Wuhan University

Scientometrics, 2018, vol. 117, issue 1, No 13, 226 pages

Abstract: 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.

Keywords: Word-topic distribution; Semantic shifts; Semantic analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11192-018-2843-2

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