Learning device, extraction device, and learning method for tagging description portions in a document

An extraction apparatus comprising one or more processors configured to perform operations comprising:

• obtaining training data that includes a plurality of description portions of a natural language document and a plurality of tags, wherein each description portion includes a set of words and each description portion is associated with a tag that was provided to the description portion in advance;
• calculating an information gain for each word included in the training data that indicates a degree of relevance of the word to the plurality of tags for the word;
• deleting, from the training data, words whose degrees of relevance do not satisfy a threshold to obtain pre-processed training data;
• learning the pre-processed training data and the information gain for each word that indicates the degree of relevance of the word to the plurality of tags;
• generating a list of conditional probabilities indicating a trend for attaching the plurality of description portions with the plurality of tags;
• receiving test data;
• tagging, based on the list of conditional probability, description content of the test data by attaching a tag, included in the plurality of tags, to a corresponding portion of the description content;
• extracting a test item from the test data based on the attached tags of the description content;
• and
• outputting the test item into a testing apparatus for testing.

A learning method to be executed by a learning apparatus, the learning method comprising:

• obtaining training data that includes a plurality of description portions of a natural language document and a plurality of tags, wherein each description portion includes a set of words and each description portion is associated with a tag that was provided to the description portion in advance;
• calculating an information gain for each word included in the training data that indicates a degree of relevance of the word to the plurality of tags for the word;
• deleting, from the training data, words whose degrees of relevance do not satisfy a threshold to obtain pre-processed training data;
• learning the pre-processed training data and the information gain for each word that indicates the degree of relevance of the word to the plurality of tags;
• generating a list of conditional probabilities indicating a trend for attaching the plurality of description portions with the plurality of tags;
• receiving test data;
• tagging, based on the list of conditional probability, description content of the test data by attaching a tag, included in the plurality of tags, to a corresponding portion of the description content;
• extracting a test item from the test data based on the tagged description content;
• and
• outputting the test item into a testing apparatus for testing.