  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. The constructed semantic
features, in combination with fast linear classifiers, tested against strong
baselines such as hierarchical attention neural networks, achieves comparable
classification results on short text documents. The algorithm's performance is
also tested in a few-shot learning setting, indicating that the inclusion of
semantic features can improve the performance in data-scarce situations. The
tax2vec capability to extract corpus-specific semantic keywords is also
demonstrated. Finally, we investigate the semantic space of potential features,
where we observe a similarity with the well known Zipf's law.
