Name
Abstract | ||
|---|---|---|
AbstractAbstractFrequency is one of the major factors for training quality word embeddings. Several studies have recently discussed the stability of word embeddings in general domain and suggested factors influencing the stability. In this work, we conduct a detailed analysis on the stability of concept embeddings in medical domain, particularly in relations with concept frequency. The analysis reveals the surprising high stability of low‐frequency concepts: low‐frequency (<100) concepts have the same high stability as high‐frequency (>1,000) concepts. To develop a deeper understanding of this finding, we propose a new factor, the noisiness of context words, which influences the stability of medical concept embeddings regardless of high or low frequency. We evaluate the proposed factor by showing the linear correlation with the stability of medical concept embeddings. The correlations are clear and consistent with various groups of medical concepts. Based on the linear relations, we make suggestions on ways to adjust the noisiness of context words for the improvement of stability. Finally, we demonstrate that the linear relation of the proposed factor extends to the word embedding stability in general domain. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/asi.24411 | Periodicals |
DocType | Volume | Issue |
Journal | 72 | 3 |
ISSN | Citations | PageRank |
2330-1635 | 0 | 0.34 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grace E. Lee | 1 | 0 | 0.34 |
| Aixin Sun | 2 | 3071 | 156.89 |