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tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
2020
Computer Speech and Language
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, age, news topics, drug side effects and drug effectiveness.
doi:10.1016/j.csl.2020.101104
fatcat:q6mkjy3orjap5gswytzr3lcwli