Deep learning approaches
Neural networks, and especially the large neural networks generally referred to as deep learning, have become very popular for NLU in the past few years because they significantly improve the accuracy of earlier methods.
The basic concept behind neural networks is that they consist of layers of connected units, called neurons in analogy to the neurons in animal nervous systems. Each neuron in a neural net is connected to other neurons in the neural net. If a neuron receives the appropriate inputs from other neurons, it will fire, or send input to another neuron, which will in turn fire or not fire depending on other inputs that it receives. During the training process, weights on the neurons are adjusted to maximize classification accuracy.
Figure 3.5 shows an example of a four-layer neural net performing a sentiment analysis task. The neurons are circles connected by lines. The first layer, on the left, receives a text input. Two hidden layers of neurons...