An analysis of hierarchical text classification using word embeddings

Efficient distributed numerical word representation models (word embeddings) combinec 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 stud investigates the application of those models and algorithms on this specific problem b3 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 fastTextwith publicly available data and evaluated them with measures specifically appropriate fot the hierarchical context. FastText achieved an LcAF(1) of 0.893 on a single-labeled version o the RCV1 dataset. An analysis indicates that using word embeddings and its flavors is very promising approach for HTC. (C) 2018 Elsevier Inc. All rights reserved