  Slot filling is an important problem in Spoken Language Understanding (SLU)
and Natural Language Processing (NLP), which involves identifying a user's
intent and assigning a semantic concept to each word in a sentence. This paper
presents a word feature vector method and combines it into the convolutional
neural network (CNN). We consider 18 word features and each word feature is
constructed by merging similar word labels. By introducing the concept of
external library, we propose a feature set approach that is beneficial for
building the relationship between a word from the training dataset and the
feature. Computational results are reported using the ATIS dataset and
comparisons with traditional CNN as well as bi-directional sequential CNN are
also presented.
