Sentence examples for representations of text from inspiring English sources

Words such as no, none, or not are often neglected in vector representations of text.

Recently, it was demonstrated that convolutional neural networks can be used to train classifiers from character-level representations of text.

Namely, our embedding results in matrix representations of text instances where one dimension is much greater than the other.

In this paper, we propose a new method of creating character-level representations of text to reduce the computational costs associated with training a deep convolutional neural network.

Zhang et al. [1] demonstrated using deep CNNs with character-level data representations of text outperforms approaches using higher level human generated text representations such as bag-of-words.

Additionally, they have recently been demonstrated to be able to learn high-level text concepts from character-level representations of text [1] in a manner similar to how they learn features from and can classify images.

Reading researchers (e.g., Trabasso and van den Broek1985; Trabasso et al.1984; Warren et al.1979) and linguists (e.g., Noordman and de Blijzer2000; Schleppegrell and Achugar2003; Veel and Coffin1996) not only agree that causation is an important ordering concept for text comprehension but that variations on causal constructions impact readers' mental representations of texts.

It's not so much a melody as a representation of text in another dimension.

The Unicode Consortium, the organisation that co-ordinated the development of Unicode, the industry standard for the representation of text, has accepted the new emojis as candidates for Unicode 9, an update which will be released in mid-2016.

An enhanced S-VSM model will be considered for the constructive representation of text documents.

Most of the existing methods leverage the power of deep learning to produce a representation of text.