Context (archaeology)

Linguistics

Philosophy

Pure mathematics

Biology

Construct (python library)

Field (mathematics)

Economics

Computer science

Abstraction

Language model

Paleontology

Natural language processing

Task (project management)

Pattern recognition (psychology)

Mathematics

Management

Feature (linguistics)

Epistemology

Artificial intelligence

Programming language

Deep learning

Artificial neural network

Convolutional neural network

Recurrent neural network

Text classification is a classic task in the field of natural language processing. however, the existing methods of text classification tasks still need to be improved because of the complex abstraction of text semantic information and the strong relecvance of context. In this paper, we combine the advantages of two traditional neural network model, Long Short-Term Memory(LSTM) and Convolutional Neural Network(CNN). LSTM can effectively preserve the characteristics of historical information in long text sequences, and extract local features of text by using the structure of CNN. We proposes a hybrid model of LSTM and CNN, construct CNN model on the top of LSTM, the text feature vector output from LSTM is further extracted by CNN structure. The performance of the hybrid model is compared with that of other models in the experiment. The experimental results show that the hybrid model can effectively improve the accuracy of text classification.

LSTM-CNN Hybrid Model for Text Classification