Sentence examples for representations text from inspiring English sources

In this case different representations (text and MeSH) are used, this relates to cross-lingual relevance models in which query and documents are formulated in different languages (Lavrenko et al., 2002).

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.

This paper describes a parser in progress which is directed to generating representations for text understanding.

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.

To test the understandability and reading speed of regulatory requirements using this representation and visualization method, a survey was conducted to compare different representations, namely, text, logic-based, and tree-based.

To differentiate between the added value of the conceptual and textual representations, separate text and concept indices are created.

Our primary rationale for making available these linguistic variability data is that they will provide computational linguists with a useful resource for training and evaluating systems designed to automatically extract property-norm-like semantic feature representations from text corpora.

Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components.