论文标题
Sememnn：用于文本分类的基于语义矩阵的内存神经网络
SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification
论文作者
论文摘要
文本分类是将标签分配给用自然语言编写的文档的任务，并且它具有许多真实的应用程序，包括情感分析以及传统的主题分配任务。在本文中，我们通过端到端学习方式为基于语义矩阵的内存神经网络提出了5种不同的配置，并评估了我们在两份新闻文章（AG News，Sogou News）上提出的方法。我们提出的方法的最佳性能优于文本分类任务上的基线VDCNN模型，并为学习语义提供了更快的速度。此外，我们还评估了小型数据集上的模型。结果表明，与小型数据集上的VDCNN相比，我们提出的方法仍然可以取得更好的结果。本文将出现在2020年IEEE第14届国际语义计算会议（ICSC 2020），加利福尼亚州圣地亚哥，2020年。
Text categorization is the task of assigning labels to documents written in a natural language, and it has numerous real-world applications including sentiment analysis as well as traditional topic assignment tasks. In this paper, we propose 5 different configurations for the semantic matrix-based memory neural network with end-to-end learning manner and evaluate our proposed method on two corpora of news articles (AG news, Sogou news). The best performance of our proposed method outperforms the baseline VDCNN models on the text classification task and gives a faster speed for learning semantics. Moreover, we also evaluate our model on small scale datasets. The results show that our proposed method can still achieve better results in comparison to VDCNN on the small scale dataset. This paper is to appear in the Proceedings of the 2020 IEEE 14th International Conference on Semantic Computing (ICSC 2020), San Diego, California, 2020.
