Text Classification With Document Embeddings
CHINESE COMPUTATIONAL LINGUISTICS AND NATURAL LANGUAGE PROCESSING BASED ON NATURALLY ANNOTATED BIG DATA, CCL 2014(2014)
摘要
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.
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关键词
Support Vector Machine, Sentiment Analysis, Parse Tree, Vector Space Model, Neural Network Architecture
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