# A C-LSTM Neural Network for Text Classification

arXiv: https://arxiv.org/abs/1511.08630 · HTML: https://mlsanity.com/paper/1511.08630/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2015.

## Built on by (most cited first, 30 of 54)

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- [Classifying YouTube Comments Based on Sentiment and Type of Sentence](https://mlsanity.com/paper/2111.01908/) (2021)
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- [DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification](https://mlsanity.com/paper/2307.06005/) (2023)
- [SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification](https://mlsanity.com/paper/2312.06088/) (2023)

## Abstract (arXiv)

Neural network models have been demonstrated to be capable of achieving remarkable performance in sentence and document modeling. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modeling tasks, which adopt totally different ways of understanding natural languages. In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification. C-LSTM utilizes CNN to extract a sequence of higher-level phrase representations, and are fed into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation. C-LSTM is able to capture both local features of phrases as well as global and temporal sentence semantics. We evaluate the proposed architecture on sentiment classification and question classification tasks. The experimental results show that the C-LSTM outperforms both CNN and LSTM and can achieve excellent performance on these tasks.