# Word-Class Embeddings for Multiclass Text Classification

arXiv: https://arxiv.org/abs/1911.11506 · HTML: https://mlsanity.com/paper/1911.11506/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification](https://mlsanity.com/paper/2110.14764/) (2021)
- [API-Miner: an API-to-API Specification Recommendation Engine](https://mlsanity.com/paper/2212.07253/) (2022)
- [Utilizing Weak Supervision To Generate Indonesian Conservation Dataset](https://mlsanity.com/paper/2310.11258/) (2023)
- [Framework of Voting Prediction of Parliament Members](https://mlsanity.com/paper/2505.12535/) (2025)

## Abstract (arXiv)

Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few. In supervised tasks such as multiclass text classification (the focus of this article) it seems appealing to enhance word representations with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings, they substantially facilitate the training of deep-learning models in multiclass classification by topic. We show empirical evidence that WCEs yield a consistent improvement in multiclass classification accuracy, using four popular neural architectures and six widely used and publicly available datasets for multiclass text classification. Our code that implements WCEs is publicly available at https://github.com/AlexMoreo/word-class-embeddings