tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
Abstract
null null The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may improve the performance and robustness of the learned classifiers. We propose tax2vec, a parallel algorithm for constructing taxonomy-based features, and demonstrate its use on six short text classification problems: prediction of gender, personality type,...
Paper Details
Title
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
Published Date
Jan 1, 2021
Journal
Volume
65
Pages
101104
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