Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling

Understand

Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text.

• RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix.
• And this matrix includes two dimensions: the time-step dimension and the feature vector dimension.
• Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector.