2.1. WORD REPRESENTATION USING CNNS AS SHOWN IN FIG. 3, OUR WO...

3.2.1. Word representation using CNNs

As shown in Fig. 3, our word representations employ both handcrafted and

automatically learned features.

 Handcrafted features. We use the POS tag of the word and multiple features

that check whether the word contains special characters, whether the word is a

number, and look at capitalization patterns of the word.

 Automatically learned features. We use both word embeddings and

character embeddings. Convolutional neural networks are then used to extract

features from the matrix formed from character embeddings.

Fig. 3. Word representation using CNNs

The final representation of a word is the concatenation of three components: