Learning Concept Embeddings from Temporal Data

被引:0
|
作者
Meyer, Francois [1 ]
van der Merwe, Brink [1 ]
Coetsee, Dirko [2 ]
机构
[1] Stellenbosch Univ, Comp Sci Div, Stellenbosch, South Africa
[2] Praelexis, Stellenbosch, South Africa
关键词
Deep Learning; Natural Language Processing; Word Embeddings; Temporal Data; Skip-Gram;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Word embedding techniques can be used to learn vector representations of concepts from temporal datasets. Previous attempts to do this amounted to applying word embedding techniques to event sequences. We propose a concept embedding model that extends existing word embedding techniques to take time into account by explicitly modelling the time between concept occurrences. The model is implemented and evaluated using medical temporal data. It is found that incorporating time into the learning algorithm can improve the quality of the resulting embeddings, as measured by an existing methodological framework for evaluating medical concept embeddings.
引用
收藏
页码:1378 / 1402
页数:25
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