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Tracking word semantic change in biomedical literature
Journal article   Peer reviewed

Tracking word semantic change in biomedical literature

Erjia Yan and Yongjun Zhu
International journal of medical informatics (Shannon, Ireland), v 109, pp 76-86
Jan 2018
DOI: https://doi.org/10.1016/j.ijmedinf.2017.11.006
PMID: 29195709

Abstract

Topic modeling Skip-gram Word2vec PubMed Semantic change
•The paper examines word semantic change using a 30-year biomedical literature data.•A set of representative words is obtained based on frequency and topic distribution.•A skip-gram language model is used to measure global and local semantic change.•Word meanings are more stable in the 2000s than they were in the 1980s and 1990s.•No evidence is found to support the law of parallel change or the law of conformity. Up to this point, research on written scholarly communication has focused primarily on syntactic, rather than semantic, analyses. Consequently, we have yet to understand semantic change as it applies to disciplinary discourse. The objective of this study is to illustrate word semantic change in biomedical literature. To that end, we identify a set of representative words in biomedical literature based on word frequency and word-topic probability distributions. A word2vec language model is then applied to the identified words in order to measure word- and topic-level semantic changes. We find that for the selected words in PubMed, overall, meanings are becoming more stable in the 2000s than they were in the 1980s and 1990s. At the topic level, the global distance of most topics (19 out of 20 tested) is declining, suggesting that the words used to discuss these topics are stabilizing semantically. Similarly, the local distance of most topics (19 out of 20) is also declining, showing that the meanings of words from these topics are becoming more consistent with those of their semantic neighbors. At the word level, this paper identifies two different trends in word semantics, as measured by the aforementioned distance metrics: on the one hand, words can form clusters with their semantic neighbors, and these words, as a cluster, coevolve semantically; on the other hand, words can drift apart from their semantic neighbors while nonetheless stabilizing in the global context. In relating our work to language laws on semantic change, we find no overwhelming evidence to support either the law of parallel change or the law of conformity.

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Domestic collaboration
Web of Science research areas
Computer Science, Information Systems
Health Care Sciences & Services
Medical Informatics
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