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In conversation, it is the orderly flow of ideas wherein certain topics are introduced and grouped in organized manners, and additional information expands on these topics by saying something about them.
One example of a discourse is a book wherein the lowest level of a topic exists in sentences. Generally, a sentence is a grammatically self-contained language unit consisting of a word or a syntactically related group of words that express an assertion, a question, a command, a wish, or an exclamation; that in writing usually begins with a capital letter and concludes with appropriate ending punctuation; and that in speaking is phonetically distinguished by various patterns of stress, pitch and pause.
Each sentence in a discourse can be said to have a topic, explicitly stated or implied, and a focus, or something that is being said about the topic. In general, theme identifies which topic is really being discussed and what is being said about that topic.
To understand the thematic information in a sentence, an analysis method is needed that is able to experience all of the subtle nuances that a writer conveys to a reader in less tangible ways.
The human mind does not understand information by analyzing the grammatical content of a sentence. Many sentences are identical in grammatical context but are very different because of the specific selection of words and what additional facets of understanding the words add to the understanding of the sentence.
The difference does not just influence the topics by introducing another different idea, but also influences the level of importance that each word has in the sentence by indicating new, extra-grammatical, thematic contexts. Therefore, prior art systems that determine the importance of theme by counting the number of times words appear in a document do not accurately determine theme.
The theme vector processor receives thematic tags for words and phrases in the input discourse, wherein the thematic tags indicate applicability of thematic constructions that define content of discourse. In addition, theme terms are identified based on the content carrying words of the input discourse.
The theme vector processor identifies themes of the input discourse, including identifying the relative importance of the themes in the input discourse, based on the thematic tags and the theme terms. Specifically, the theme vector processor generates a theme strength for the theme terms.
The theme strength indicates relative thematic importance for the theme terms in the input discourse. In one embodiment, the theme vector processor generates theme concepts for each theme term in the input discourse through use of a knowledge catalog.
The knowledge catalog includes independent and parallel static ontologies arranged in a hierarchical structure. The static ontologies contain knowledge concepts and present a world view of knowledge.
The theme vector processor utilizes the static ontologies to generate a theme concept for a theme term by extracting a knowledge concept from a higher level node in the hierarchical structure of the static ontologies. Other features and advantages of the present invention will be apparent from the accompanying drawings, and from the detailed description that follows below.
The knowledge catalog includes a set of static ontologies to define knowledge. The knowledge catalog may be characterized as including several static ontologies because the knowledge catalog provides multiple views, characterizations, and organizations of concepts.
As is explained below, the knowledge catalog contains a broad coverage of concepts to provide a world view of knowledge. The static ontologies are configured as hierarchical structures such that knowledge concepts are organized from high level generalized concepts down to more specific concepts.
The static ontologies are parallel and independent of each other. As shown in FIG. However, the specific concepts contained within each static ontology hierarchical structure are set forth in the Static Ontologies.
The Static Ontologies that follow include a plurality of static ontologies configured in accordance with the preferred embodiment of the present invention.
Each ontology shown in FIG. Generally, the static ontologies of the present invention includes no more than six levels. The actual configuration, structure and orientation of a particular ontology is dependent upon the subject matter or field of the ontology. Therefore, each ontology in the set of static ontologies of the present invention contain a different point of view.
The different points of view for the static ontologies result in different approaches or different ways of viewing the knowledge concepts in the different static ontologies.Informed by current rhetorical theory and an organization that reflects a view of writing as a process, Strategies for Successful Writing provides the necessary strategies to help you produce effective post-secondary writing, regardless of your discipline.
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