Semantic analysis in word vector spaces with ICA and feature selection
Tiina Lindh‐Knuutila, Jaakko J. Väyrynen, Timo Honkela · 2012
In this article, we test a word vector space model using direct evaluation methods. We show that independent component analysis is able to automatically produce meaning-ful components that correspond to semantic category labels. We also study the amount of features needed to represent a category using feature selection with syntactic and semantic category test sets. 1