# MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification

arXiv: https://arxiv.org/abs/1603.00968 · HTML: https://mlsanity.com/paper/1603.00968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2016.

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

- [Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets](https://mlsanity.com/paper/2211.16878/) (2022)
- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification](https://mlsanity.com/paper/2303.09115/) (2023)
- [An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool](https://mlsanity.com/paper/2603.11770/) (2026)

## Abstract (arXiv)

We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification. MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.