  Efficient distributed numerical word representation models (word embeddings)
combined with modern machine learning algorithms have recently yielded
considerable improvement on automatic document classification tasks. However,
the effectiveness of such techniques has not been assessed for the hierarchical
text classification (HTC) yet. This study investigates the application of those
models and algorithms on this specific problem by means of experimentation and
analysis. We trained classification models with prominent machine learning
algorithm implementations---fastText, XGBoost, SVM, and Keras' CNN---and
noticeable word embeddings generation methods---GloVe, word2vec, and
fastText---with publicly available data and evaluated them with measures
specifically appropriate for the hierarchical context. FastText achieved an
${}_{LCA}F_1$ of 0.893 on a single-labeled version of the RCV1 dataset. An
analysis indicates that using word embeddings and its flavors is a very
promising approach for HTC.
