continuous bag of words(CBOW) and skip-gram algorithm [@mikolov2013efficient].

Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.

### Convolutional Neural Network

As shown in figure \[fig:traditional-convolutional-network\], Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery and NLP related tasks. Same as neural network, CNN are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. A Convolutional layer is composed by four consecutive operations: Convolution, subsampling(pooling), activation and dropout. The convolution layer can help select features so that CNN requires minimal pre-processing compared to other deep learning models. The core part of the Convolutional neural network is the convolution filter.

Convolution filters leverage two important ideas that can help improve a machine learning system: sparse interaction and parameter sharing. Sparse interaction contrasts with traditional neural networks where each output is interactive with each input. As a result, the output is only interactive with a narrow window of the input. Parameter sharing refers 