# Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

**Authors:** Ji Young Lee, Franck Dernoncourt  
**arXiv:** [1603.03827](https://arxiv.org/abs/1603.03827)  
**Date:** 2016-03-12  
**Summary page:** https://www.summarizepaper.com/fr/paper/1603.03827v1/

## Abstract

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.