Document classification using unsupervised text analysis with concept extraction

A computer system, the computer system comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
• obtaining a generic description for a target document, wherein obtaining the generic description comprises:
• extracting concepts from headings and descriptions of the target document using natural language processing;
• matching the extracted concepts to generic abstracts obtained from an abstract database, where the abstract database is independent and separate from the target document;
• processing the generic abstracts to lemmatize words and remove common words to form the generic description;
• processing an available series of technical classifications;
• performing bidirectional analysis to determine a most relevant technical classification for the target document based on the generic description for the target document by summing occurrences of corresponding words within the generic description for the target document and aggregate descriptions of each of the technical classifications in the available series of technical classifications and calculating scores for each of the technical classifications in the available series of technical classifications based on the sum of occurrences;
• and
• assigning the most relevant technical classification to the target document, wherein the most relevant technical classification corresponds to the technical classification with a highest calculated score, wherein the highest calculated score corresponds to a highest volume of word matches and a highest association between level descriptions of the technical classifications in the available series of technical classifications as compared to the generic description of the target document.

A computer program product comprising:

• one or more computer-readable storage media;
• program instructions stored on the one or more computer-readable storage media to perform operations comprising:
• obtaining a generic description for a target document, wherein obtaining the generic description comprises:
• extracting concepts from headings and descriptions of the target document using natural language processing;
• matching the extracted concepts to generic abstracts obtained from an abstract database, where the abstract database is independent and separate from the target document;
• processing the generic abstracts to lemmatize words and remove common words to form the generic description;
• processing an available series of technical classifications;
• performing bidirectional analysis to determine a most relevant technical classification for the target document based on the generic description for the target document by summing occurrences of corresponding words within the generic description for the target document and aggregate descriptions of each of the technical classifications in the available series of technical classifications and calculating scores for each of the technical classifications in the available series of technical classifications based on the sum of occurrences;
• and
• assigning the most relevant technical classification to the target document, wherein the most relevant technical classification corresponds to the technical classification with a highest calculated score, wherein the highest calculated score corresponds to a highest volume of word matches and a highest association between level descriptions of the technical classifications in the available series of technical classifications as compared to the generic description of the target document.