W-RNN: News text classification based on a Weighted RNN
This work addresses text classification for news data, but it appears incremental as it builds on existing RNN methods with a weighting mechanism.
The paper tackled the semantic constraint problem in text classification by proposing a weighted recurrent neural network (W-RNN) to extract serialization semantic information, and it demonstrated superiority over four baseline methods in Precision, Recall, F1, and loss values on a news dataset.
Most of the information is stored as text, so text mining is regarded as having high commercial potential. Aiming at the semantic constraint problem of classification methods based on sparse representation, we propose a weighted recurrent neural network (W-RNN), which can fully extract text serialization semantic information. For the problem that the feature high dimensionality and unclear semantic relationship in text data representation, we first utilize the word vector to represent the vocabulary in the text and use Recurrent Neural Network (RNN) to extract features of the serialized text data. The word vector is then automatically weighted and summed using the intermediate output of the word vector to form the text representation vector. Finally, the neural network is used for classification. W-RNN is verified on the news dataset and proves that W-RNN is superior to other four baseline methods in Precision, Recall, F1 and loss values, which is suitable for text classification.