Sentence examples for information sentences from inspiring English sources

The key part of the ExaCT system is its IE engine, which extracts pieces of information (sentences and/or text fragments) from a trial publication to fill slots in the pre-defined template.

Particularly in terms of the dynamic dual pathway model the authors argue, that segmental information (phonemes, syntactic and lexial semantic elements) are primarily processed in a left hemisphere temporo-frontal pathway whereas suprasegmental information (sentence level prosody) is processed in a right hemispheric temporo-frontal pathway.

Knight et al. [ 5] showed that Machine Learning could be applied to label structural information of sentences (i.e. Introduction, Method, Results or Conclusion) using a combination of relative sentence position and word distribution.

What is the basic scheme by which symbolic information (like sentences) are stored?

They are considered as unimportant information between sentences.

Paradigm cases of structures with semantic information — pictures, sentences, programs — are built by the thought and action of intelligent agents.

Recursive NNs (RecNNs), proposed by [23], construct neural networks on the basis of parse trees and are able to extract fine-grained information of sentences.

The analogy to brain connectivity is tight: we wish to extract information from sentences akin to 'brain region A connects to brain region B'.

The syntactic and semantic information of sentences are stored in our parse tree database and extraction is performed by means of database queries.

In the gene interaction task, one must extract information from sentences such as 'gene A interacts with gene B' (to give a toy example).

Due to the hierarchical nature of the syntactic and semantic information of sentences, writing SQL queries for extraction can be complicated with the use of numerous table joins.