Sentence examples for representation of sentence from inspiring English sources

We believe that such a visual representation of sentence structure is helpful for deeper understanding of human language grammar rules.

In essence, the representation of sentences in statistical semantics consists of the sentences themselves.

In this work, we focus on reducing the impact of noisy instances and learning a better representation of sentences.

It is not hard to see in this an informal anticipation of the representation of sentences in terms of a combination of universal quantifier and object- and predicate-variables.

At the core of our approach to event extraction are graph representations of sentence syntax and semantics.

The variants of phonemes that occur in phonetic representations of sentences are known as allophones.

But this does not prevent practical reasoning, either by direct use of such quantifiers in the logical representations of sentences (an approach in the spirit of natural logic), or by reducing them to set-theoretic or mereological relations within an FOL framework.

According to this hypothesis the process of repeating a sentence starts from a conceptual (meaning-based) representation of the sentence to be recalled and essentially involves all levels of the language production system (Bock & Levelt, 1994).

Most critically in relation to the Regeneration Hypothesis, the listener after hearing a sentence creates a conceptual (rather than form-based) representation of the sentence that is to be recalled.

Potter and Lombardi (1990) provided support for the Regeneration Hypothesis through a series of experiments showing that the patterns of intrusions that occur in sentence repetition tasks are consistent with participants using a conceptual representation of the sentence to be repeated.

In Fig. 3, we demonstrate an example of transforming an abstract into a vector space representation of each sentence, by using the above normalization process.