12. A training data generation method used for generating training data utilized in the creation of characteristic expression extraction rules, the method comprising the steps of: assigning labels to labeled and unlabeled training data candidates by referring to an incomplete ground truth corpus, clustering a plurality of training data candidates including annotation errors and assigned labels indicating annotation classes based on feature values containing respective context information, and by referring to each cluster obtained using the clustering results in said clustering step, obtaining the distribution of the labels of the training data candidates within the cluster, identifying training data candidates that meet a preset condition based on the obtained distribution, and generating training data using the identified training data candidates, wherein the context information is individual words, modifying or modified phrases in the vicinity of the units that constitute the training data candidates or training data, and the training data is utilized in the creation of characteristic expression extraction rules.
12. A training data generation method used for generating training data utilized in the creation of characteristic expression extraction rules, the method comprising the steps of: assigning labels to labeled and unlabeled training data candidates by referring to an incomplete ground truth corpus, clustering a plurality of training data candidates including annotation errors and assigned labels indicating annotation classes based on feature values containing respective context information, and by referring to each cluster obtained using the clustering results in said clustering step, obtaining the distribution of the labels of the training data candidates within the cluster, identifying training data candidates that meet a preset condition based on the obtained distribution, and generating training data using the identified training data candidates, wherein the context information is individual words, modifying or modified phrases in the vicinity of the units that constitute the training data candidates or training data, and the training data is utilized in the creation of characteristic expression extraction rules. 17. The training data generation method according to claim 12 , wherein in Step (a), during clustering, the clustering condition is configured such that one or more conditions are met among the following conditions: the distribution of the labels within each cluster varies between the clusters, there is a label with the highest percentage within each cluster, the number of training data candidates within each cluster exceeds a specified number, and the number of clusters is less than a specified number.