Text-classification code, system and method

An automated system for classifying a target document in the form of a digitally encoded text as belonging to one or more of a plurality of different classes comprising

• (1) a computer,
• (2) accessible by said computer, a database of word records, where each record includes text identifiers of the library texts that contain the word, associated library and classification identifiers for each text, and optionally, one or more selectivity values for each word, where the selectivity value of a term in a library of texts in a field is related to the frequency of occurrence of the term in said library, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively,
• (3) a computer readable code which is operable, under the control of said computer, to perform steps comprising:
• (a) for each of a plurality of terms selected from one of (i) non-generic words in the document, (ii) proximately arranged word groups in the document, and (iii) a combination of (i) and (ii), determining a selectivity value calculated as the frequency of occurrence of the term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively,
• (b) representing the document as a vector of terms, where the coefficient assigned to each term is a function of the selectivity value determined for the term,
• (c) determining for each of a plurality of sample texts, a match score related to the number of descriptive terms present in or derived from the text that match those in the target document, where each of the plurality of sample texts has an associated classification identifier that identifies the one of more different classes to which the text belongs,
• (d) selecting one or more of the sample texts having the highest match scores,
• (e) recording the one or more classification identifiers associated with the one or more sample texts having the highest match scores, and
• (f) associating the one or more classification identifiers from step (e) with the target document, thereby to classify the target document as belonging to one or more classes represented by at least one of the classification identifiers from step (e).

Computer readable code for use with an electronic computer and a database word records in classifying a target document in the form of a digitally encoded text as belonging to one or more of a plurality of different classes, where each record in the word records database includes text identifiers of the library texts that contain the word, an associated library identifier for each text, an associated classification identifier for each text, and optionally, one or more selectivity values for each word, where the selectivity value of a term in a library of texts in a field is related to the frequency of occurrence of the term in said library, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively, said code being operable, under the control of said computer, to perform steps comprising:

• (a) for each of a plurality of terms selected from one of (i) non-generic words in the document, (ii) proximately arranged word groups in the document, and (iii) a combination of (i) and (ii), determining a selectivity value calculated as the frequency of occurrence of the term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively,
• (b) representing the document as a vector of terms, where the coefficient assigned to each term is a function of the selectivity value determined for the term,
• (c) determining for each of a plurality of sample texts, a match score related to the number of descriptive terms present in or derived from the text that match those in the target document, where each of the plurality of sample texts has an associated classification identifier that identifies the one of more different classes to which the text belongs,
• (d) selecting one or more of the sample texts having the highest match scores,
• (e) recording the one or more classification identifiers associated with the one or more sample texts having the highest match scores, and
• (f) associating the one or more classification identifiers from step (e) with the target document, thereby to classify the target document as belonging to one or more classes represented by at least one of the classification identifiers from step (e).