Abstract
The objective of this research-in-progress paper is to reveal word semantic change and verify whether the change conforms to the law of differentiation or the law of parallel change. This paper identifies a set of representative words in biomedical literature based on word frequency and word-topic probability distributions. It employs a skip-gram word2vec model to the identified words to measure word-level semantic changes. This paper finds no overwhelming evidence to support either the law of differentiation or the law of parallel change.
| Original language | English |
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| Pages | 1342-1345 |
| Number of pages | 4 |
| Publication status | Published - 2017 |
| Event | 16th International Conference on Scientometrics and Informetrics, ISSI 2017 - Wuhan, China Duration: 2017 Oct 16 → 2017 Oct 20 |
Other
| Other | 16th International Conference on Scientometrics and Informetrics, ISSI 2017 |
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| Country/Territory | China |
| City | Wuhan |
| Period | 17/10/16 → 17/10/20 |
Bibliographical note
Funding Information:This project was made possible in part by the Institute of Museum and Library Services (Grant Award Number: RE-07-15-0060-15), for the project titled “Building an entity-based research framework to enhance digital services on knowledge discovery and delivery”.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Applied Mathematics
- Modelling and Simulation
- Statistics and Probability
- Management Science and Operations Research