SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification
This work addresses text classification for applications like sentiment analysis and topic assignment, but it is incremental as it builds on existing memory neural network approaches.
The paper tackled text classification by proposing SeMemNN, a semantic matrix-based memory neural network with five configurations, achieving better performance and faster learning speed than VDCNN baselines on news article datasets like AG and Sogou news, including on small-scale datasets.
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.