Method for predicting negative example, system for detecting incorrect wording using negative example prediction

A recording medium recorded with a program for implementing a processing method for predicting whether data that is not yet known to be positive or negative with respect to a certain problem is positive or negative on a computer, comprising:

• a positive example accessing step of accessing a positive example data storage unit pre-storing groups of positive example data constituting correction examples for the problem;
• an existence determination step for determining whether or not the data exists in the positive example data groups;
• an appearance probability calculating step for calculating typical probability of appearance of the data when the data does not exist in the positive example data groups;
• and
• an negative example likelihood calculation step for calculating a probability of appearance of the data in the positive example data group based on the typical probability of appearance and taking the probability as a likelihood of an negative example.

A recording medium recorded with a program for implementing processing to detect incorrect wording using a method for predicting negative examples on a computer, comprising:

• a positive example data accessing step of accessing a positive example data storage unit pre-storing groups of positive example data constituting correctly worded data;
• an existence determination step for determining whether or not the inputted wording exists in the positive example data groups;
• an appearance probability calculating step for calculating typical probability of appearance of the inputted wording when inputted wording data does not exist in the positive example data groups;
• and
• an negative example likelihood calculation step for calculating a probability of appearance of the inputted wording in the positive example data group based on the typical probability of appearance and taking the probability as a likelihood of an negative example.

A recording medium recorded with a program for implementing processing to extract a embedded clause constituting a non-case relational relative clause using a method for predicting negative examples on a computer, comprising:

• a positive example data accessing step of accessing a positive example data storage unit pre-storing groups of positive example data constituting case relational relative clauses;
• an existence determination step for determining whether or not the inputted embedded example exists in the positive example data group;
• an appearance probability calculating step for calculating typical probability of appearance of the embedded clause when the embedded clause data does not exist in the positive example data groups;
• and
• an negative example likelihood calculation step for calculating a probability of appearance of the embedded clause in the positive example data group based on the typical probability of appearance and taking the probability as a likelihood of an negative example.

A method for detecting incorrect wording using a supervised machine learning method, comprising the steps of:

• extracting pairs of feature and solutions from supervised data including correctly worded positive example data and incorrectly worded negative example data;
• performing machine learning taking pairs of extracted features and solutions as borrowing supervised data and storing learning results in a learning results data storage unit;
• and
• extracting features from inputted data and detecting incorrect wording based on the learning results saved in the learning results data storage unit.

A recording medium recorded with a program for implementing processing to detect incorrect wording using a supervised machine learning method, comprising the steps of:

• processing for extracting pairs of features and solutions from supervised data including correctly worded positive example data and incorrectly worded negative example data;
• performing machine learning processing taking pairs of features and solutions as borrowing supervised data and storing learning results in a learning results data storage unit;
• and
• processing for extracting features from inputted data and detecting incorrect wording in the data based on the learning results.

A processing method for detecting incorrect wording comprising the steps of:

• calculating typical probability of appearance for examples when inputted examples to not exist in prepared correctly worded positive example data;
• calculating probability of the example appearing in the positive example data based on the typical probability of appearance, and taking the example as negative example data when the probability exceeds a prescribed threshold value;
• extracting pairs of features and solutions from supervised data including positive example data and negative example data;
• performing machine learning processing taking pairs of features and solutions as borrowing supervised data, and storing learning results in a learning results data storage unit;
• and
• extracting features from inputted data and detecting incorrect wording in the data based on the learning results.

A recording medium recorded with a program for implementing processing to detect incorrect wording using supervised machine learning methods, comprising the steps of:

• calculating typical probability of appearance for examples when inputted examples to not exist in prepared correctly worded positive example data;
• calculating probability of the example appearing in the positive example data based on the typical probability of appearance, and taking the example as negative example data when the probability exceeds a prescribed threshold value;
• extracting pairs of features and solutions from supervised data including positive example data and negative example data;
• performing machine learning processing taking pairs of features and solutions as borrowing supervised data, and storing learning results in a learning results data storage unit;
• and
• extracting features from inputted data and detecting incorrect wording in the data based on the learning results.