Sentence examples for syntactic parsing from inspiring English sources

Luong et al. [24] employed a recursive model to learn vector representations for phrases and used them to perform syntactic parsing.

In 2011 Collobert used a similar network architecture for syntactic parsing.

Syntactic parsing is a critical component of BioNLP applications that rely on correctly determining the sentence and phrase structure of free text.

Systems which do not follow the bag-of-word approach typically rely on syntactic parsing to obtain larger constituents and base their analysis on more complex structures.

Prosody has an immediate rather than delayed effect on a range of syntactic parsing decisions, suggesting that prosody can determine the incremental construction of a syntactic representation on the fly during sentence comprehension.

Specifically, by using syntactic parsing and sentiment dictionary, we propose a rule based approach to extract topic words of opinion sentences associated with negative sentiment, which are regarded as the advertising keywords.

These regions exhibited increased activity to processing the syntactically complex sentences only when a full syntactic parse was necessary for task completion, that is, when the sentences were semantically reversible.

Going beyond syntactic parse trees, it is possible to annotate corpora further, with information of a semantic and pragmatic nature.

We extend the mechanism of logical generalization towards syntactic parse trees and attempt to detect weak semantic signals from them.

Using syntactic parse tree-based similarity measure instead of bag-of-words and keyword frequency approach, we expect to detect a weak semantic signal otherwise unobservable.

The authors used sentence parsing in order to build a syntactic parse tree representing the syntactic constituent structure of a sentence and to build a protein-protein network from this tree.