Textual data classification method and apparatus

A method for determining weight vectors for use in classifying a natural language text within a classification system, comprising:

• providing a set of natural language descriptions correctly classified in the classification system;
• parsing the correctly classified natural language text into chunks;
• ranking the chunks into hypothesized hierarchical importance;
• identifying n-grams occurring more than once in a subset of highest ranking chunks;
• creating weight vectors based on a frequency analysis for n-grams occurring more than once;
• provisionally classifying the natural language text to classes based on the created weight vectors;
• comparing the provisional classifications with the correct classifications;
• and
• adjusting weight vectors to improve the accuracy of classification, wherein said classes relate to a meaning of said natural language text.

A method for classifying the contents of natural language text, comprising:

• receiving natural language text;
• parsing said natural language text into at least a first chunk, wherein said at least a first chunk comprises a chunk of said natural language text that does not include any punctuation marks;
• counting a number of instances of said first chunk to obtain a frequency for said first chunk;
• multiplying said frequency for said first chunk by a first vector weight assigned to said first chunk with respect to a first classification related to a first meaning of said chunk to obtain a first product;
• multiplying said frequency for said first chunk by a second vector weight assigned to said first chunk with respect to a second classification related to a second meaning of said chunk to obtain a second product;
• determining a first sum, wherein said first sum is equal to a sum of all products associated with said first classification;
• determining a second sum, wherein said second sum is equal to a sum of all products associated with said second classification;
• in response to said first sum being greater than said second sum, assigning said natural language text to said first classification;
• and
• in response to said second sum being greater than said first sum, assigning said natural language text to said second classification.

A method for assigning a relevancy weight to a chunk of natural language text with respect to a plurality of classifications, comprising:

• receiving a plurality of examples of natural language text, wherein each of said examples of natural language text belongs to at least one of a first classification and a second classification, and wherein said classifications are related to a meaning of said examples of natural language text;
• with respect to each of said examples of natural language text, parsing said natural language text into at least a first chunk, wherein said at least a first chunk comprises a natural language word having at least one textual character;
• assigning a rank to each chunk parsed from each of said examples of natural language text;
• and
• assigning a weight value to each chunk having at least a selected rank, wherein a chunk found in an example of natural language text belonging to said first classification and in an example of natural language text belonging to said second classification is assigned a first weight with respect to said first classification and a second weight corresponding to said second classification.