An imaginary search scenario where news articles are compared with historical stock prices for given stocks (around one hundred of them). The stock's percent change is calculated by the stock's opening price prior to the article's publishing date and the next opening price. Documents are searched with verb words belonging to "increase" (or similar terms) WordNet verb ontology in intransitive context along with their subject complements. Nouns (noun headwords in subjects) are grouped depending on their category in the WordNet ontology.
Similar ideas have been presented in research papers like AZFinText, which is more about using breaking financial news to predict stock prices.

This sample employs components such as a natural language parser and a semantic parser that serve as core functions in the information extraction task.
The articles and the stock quotes are accessed through Yahoo Finance RSS and stock quotes API. The test data set contains a small number of articles read during a one month period, mostly in energy/renewables sector.

Sample patterns

Verb Phrases

appreciated strongly, evolving, expand rapidly, flourished, gains, go up, grew by 11%, increase significantly, jumped by 21.5%, magnified, prospering, soared...

Noun Phrases
Using WordNet's lexicographer files categories as a top-level ontology, for example:

N6 - nouns denoting man-made objects

Australian hotels expand rapidly to catch Chinese tourism boom