Distributed representations have gained a lot of interests in natural language processing community. In this paper, we propose a method to learn document embedding with neural network architecture for text classification task. In our architecture, each document can be represented as a fine-grained representation of different meanings so that the classification can be done more accurately. The results of our experiments show that our method achieve better performances on two popular datasets.
CITATION STYLE
Huang, C., Qiu, X., & Huang, X. (2014). Text classification with document embeddings. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8801, 131–140. https://doi.org/10.1007/978-3-319-12277-9_12
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