Sentence examples for character embedded from inspiring English sources

Ms. Collins's novels are able to fuse all of these meanings into a credible character embedded in an exciting and complex story.

We build on the work of Zhang and LeCun [1] by providing a new character embedding methodology for text classification with CNNs, demonstrate that our new character embedding greatly reduces training time, memory use and improves classification performance.

We show that our character embedding greatly reduces computational costs and training time, and improves classification performance.

We demonstrate that our method of character embedding greatly reduces training time and memory use, while significantly improving classification performance.

For this purpose we propose a new character embedding offering greatly reduced memory consumption and training time.

This allows us to conduct tests for statistically significant differences in classification performance between the different character embedding methods.

We show that it outperforms the previous character embedding for the task of binary tweet sentiment classification, i.e. determining if tweets convey positive or negative sentiment.

The premise behind our representation is that instead of having a single non-zero value in our character embedding vector, we can have multiple non-zero values.

This section provides details on our data, system environment and experimental design for evaluating our character embedding and the impact of padding in convolutional layers.

The ANOVA test, Table 5, shows there are significant differences between character embedding approaches as p-value is less than 0.05.

This paper demonstrates the effectiveness of a new character embedding designed for training CNNs and explores how neural network design may be impacted by its adoption.