# An Analysis of Hierarchical Text Classification Using Word Embeddings

arXiv: https://arxiv.org/abs/1809.01771 · HTML: https://mlsanity.com/paper/1809.01771/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification](https://mlsanity.com/paper/2406.17534/) (2024)
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- [Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings](https://mlsanity.com/paper/2506.00277/) (2025)
- [Neural Language Models with Distant Supervision to Identify Major Depressive Disorder from Clinical Notes](https://mlsanity.com/paper/2104.09644/) (2021)
- [Text Classification for Predicting Multi-level Product Categories](https://mlsanity.com/paper/2109.01084/) (2021)
- [An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition](https://mlsanity.com/paper/2204.06179/) (2022)
- [HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification](https://mlsanity.com/paper/2204.08115/) (2022)
- [Yes, DLGM! A novel hierarchical model for hazard classification](https://mlsanity.com/paper/2209.04576/) (2022)
- [On the Information Content of Predictions in Word Analogy Tests](https://mlsanity.com/paper/2210.09972/) (2022)
- [Estimation of embedding vectors in high dimensions](https://mlsanity.com/paper/2312.07802/) (2023)
- [Migrating Birds Optimization-Based Feature Selection for Text Classification](https://mlsanity.com/paper/2401.10270/) (2024)
- [On Debiasing Text Embeddings Through Context Injection](https://mlsanity.com/paper/2410.12874/) (2024)

## Abstract (arXiv)

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 LCAF1 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.