LE3 .A278 1998

1998

Watters, Carolyn

Acadia University

Master of Science

Masters

Computer Science

Computer Science

With the rapidly growing amount of information on the internet, many readers face the increasingly serious problem of overwhelming incoming news data (text, photos, and video). The ability to filter away irrelevant information is becoming critical to a news delivery system. This thesis introduces a new methodology which binds a news representation object to a news document. When users perform a search to retrieve news documents, they can acquire similar ones from other resources. First this thesis presents an algorithm for creating a representation of news object. Based on the analysis of actual collections of newspaper articles, a news representation object was created which captured the regularities of the news documents. Four types of descriptive features can be extracted from news documents. They are: Person, Event Data, Event Location and Organization. Second this thesis presents an algorithm to calculate the similarity between two news objects. We assume that two documents are related if their objectsoverlap. Results indicate that the representation of news objects can be used to quickly sieve through news documents for meaningful information. The algorithms for calculating a relationship between two news objects can be used as a fast way to filter away irrelevant news documents.

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https://scholar.acadiau.ca/islandora/object/theses:2832