Text classification
Text classification is an important task in natural language processing,
which is often applied to sentiment analysis, news filtering, spam
detection, and other scenarios8. Text classification
uses features to represent raw text and provides them as inputs to
downstream classifiers. The most commonly used representation is
Word2vec9, which uses low-dimensional dense word
vectors to represent words, but it ignores the semantic relationship
between words, so it faces problems such as data sparsity and polysemy.
In recent years, deep neural networks such as convolutional neural
network (CNN) and recurrent neural network (RNN)10have been applied to extract contextual information and semantic
representation from text. The results show that the performance is
better than the traditional method. Kim et al . achieved good
results in sentence classification by using different filters to extract
multi-granularity feature sentences11. Sinha utilized
bidirectional long and short-term networks to convert words into context
embedded representations12, enabling the network to
learn contextual information in statements.