# AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

arXiv: https://arxiv.org/abs/1611.01884 · HTML: https://mlsanity.com/paper/1611.01884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection](https://mlsanity.com/paper/2301.08995/) (2023)

## Abstract (arXiv)

Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. In this work, we propose a novel framework called AC-BLSTM for modeling sentences and documents, which combines the asymmetric convolution neural network (ACNN) with the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results demonstrate that our model achieves state-of-the-art results on five tasks, including sentiment analysis, question type classification, and subjectivity classification. In order to further improve the performance of AC-BLSTM, we propose a semi-supervised learning framework called G-AC-BLSTM for text classification by combining the generative model with AC-BLSTM.