# Modeling Order in Neural Word Embeddings at Scale

**Authors:** Andrew Trask, David Gilmore, Matthew Russell  
**arXiv:** [1506.02338](https://arxiv.org/abs/1506.02338)  
**Date:** 2015-06-08  
**Summary page:** https://www.summarizepaper.com/fr/paper/1506.02338v1/

## Abstract

Natural Language Processing (NLP) systems commonly leverage bag-of-words co-occurrence techniques to capture semantic and syntactic word relationships. The resulting word-level distributed representations often ignore morphological information, though character-level embeddings have proven valuable to NLP tasks. We propose a new neural language model incorporating both word order and character order in its embedding. The model produces several vector spaces with meaningful substructure, as evidenced by its performance of 85.8% on a recent word-analogy task, exceeding best published syntactic word-analogy scores by a 58% error margin. Furthermore, the model includes several parallel training methods, most notably allowing a skip-gram network with 160 billion parameters to be trained overnight on 3 multi-core CPUs, 14x larger than the previous largest neural network.