2018/09/11 by Jichuan Zeng, Zeng, Jichuan, Jing Li +9 · 12 citations
Computer Science · #Advanced Text Analysis Techniques #Artificial intelligence #Benchmark (surveying) #Class (philosophy) #Computation and Language (cs.CL) #Computer science #ENCODE #Encoding (memory) #FOS: Computer and information sciences #Inference #Machine learning #Natural Language Processing Techniques #Natural language processing #Topic Modeling #Topic model #cs.CL
paper · pdf · doi:10.48550/arxiv.1809.03664
published in arXiv (Cornell University) (Cornell University) · EMNLP 2018
arxiv created 2018/09/11 · openalex publication_date 2018/09/11 · arxiv updated 2018/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.