a word of interest w that corresponds to a verb v and the sentence query u201c the company operates stores mostly u201d for each word input to the network two salient features are 1 its distance from the word w being labeled and 2 its distance from the verb v of interest these two features and the words themselves are the only input to the neural network tagger because the neural network tagger cannot handle text directly in step 21 the first layer of the neural network tagger converts each word in the input sentence via a lookup table dictionary into a low dimensional e g 20 dimensional vector that comprises the features that represent that word these features are learned during backpropagation if the input sentence contains n words the lookup table provides a 20 u00d7 n feature representation two more types of features are also concatenated onto those features the distance to the word w to label and the distance to the verb v both of these features are also encoded via lookup tables e g each 5 dimensional the neural network tagger will learn its own measure of distance useful for obtaining good classification accuracy in the second layer of the neural network tagger represented by steps 22 and 23 the vectors for each word in the sentence query are transformed by convolution to a matrix step 22 which includes for example k 1 columns vectors and 200 rows step 23 using this example the convolutional second layer is capable of outputting 200 features for every window of for example 3 adjoining words in the sentence query the convolutional second layer enables the neural network to deal with variable.