Visualising Latent Semantic Spaces for Sense-Making of Natural Language Text

被引：0

作者：

Semrov, Ana ^{[1
]}

Blackwell, Alan F. ^{[1
]}

Sarkar, Advait ^{[1
,2
]}

机构：

[1] Univ Cambridge, Comp Lab, Cambridge, England

[2] Microsoft Res, Cambridge, England

来源：

DIAGRAMMATIC REPRESENTATION AND INFERENCE, DIAGRAMS 2018 | 2018年 / 10871卷

关键词：

EXPLORATION;

D O I：

10.1007/978-3-319-91376-6_47

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Latent Semantic Analysis is widely used for natural language processing, but is difficult to visualise and interpret. We present an interactive visualisation that enables the interpretation of latent semantic spaces. It combines a multi-dimensional scatterplot diagram with a novel clutter-reduction strategy based on hierarchical clustering. A study with 12 non-expert participants showed that our visualisation was significantly more usable than experimental alternatives, and helped users make better sense of the latent space.

引用

页码：517 / 525

页数：9

共 44 条

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