A mechanism is provided in a data processing system for text classifier training. The data processing system receives a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier. Each text and class specification pair comprises a text and a corresponding class specification. A domain terms selector component executing on a processor of the data processing system identifies at least one domain term in the texts of the training set. A domain terms replacer component executing on a processor of the data processing system replaces the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs. A text classifier trainer component executing on a processor of the data processing system trains the text classifier machine learning model using the revised set to form a trained text classifier machine learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for text classifier training, the method comprising: receiving, by the data processing system, a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier; identifying, by a domain terms selector component executing on a processor of the data processing system, at least one domain term in the texts of the training set; replacing, by a domain terms replacer component executing on a processor of the data processing system, the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs; and training, by a text classifier trainer component executing on a processor of the data processing system, the text classifier machine learning model using the revised set to form a trained text classifier machine learning model, wherein replacing the identified domain term comprises, for each given identified domain term, determining a domain term type of the given identified domain term and replacing the given identified domain term based on selecting a replacement method associated with the domain term type by a replacement method map. 2. The method of claim 1, wherein for at least one identified domain term, the corresponding replacement term is an empty string. 3. The method of claim 1, wherein for at least one identified domain term, the corresponding replacement term is a different domain term substring comprising a description of what the identified domain term contains. 4. The method of claim 3, wherein the different domain term substring identifies an entity canonical name, an entity type, a relation, or a negative relation. 5. (canceled) 6. The method of claim 1, wherein the replacement method is identified from a set consisting of: replacing the given identified domain term with an empty string, replacing the given identified domain term with a different domain term substring, or not replacing the given identified domain term. 7. The method of claim 1, further comprising: training a plurality of text classifier machine learning models based on a plurality of replacement method maps; classifying, by the text classifier component configured with each of the plurality of the trained text classifier machine learning models, a test set of input texts to form a plurality of sets of class determinations, wherein each set of class determinations corresponds to a respective one of the plurality of replacement method maps; determining an accuracy of each of the plurality sets of class determinations; and configuring the domain terms replacer component with the replacement method map corresponding to a set of class determinations having a highest accuracy. 8. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a domain terms selector component, a domain terms replacer component, and a text classifier trainer component, wherein the computer readable program causes the computing device to: receive, by the data processing system, a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier; identify, by the domain terms selector component executing on a processor of the data processing system, at least one domain term in the texts of the training set; replace, by the domain terms replacer component executing on a processor of the data processing system, the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs; and train, by the text classifier trainer component executing on a processor of the data processing system, the text classifier machine learning model using the revised set to form a trained text classifier machine learning model, wherein replacing the identified domain term comprises, for each given identified domain term, determining a domain term type of the given identified domain term and replacing the given identified domain term based on selecting a replacement method associated with the domain term type by a replacement method map. 9. The computer program product of claim 8, wherein for at least one identified domain term, the corresponding replacement term is an empty string. 10. The computer program product of claim 8, wherein for at least one identified domain term, the corresponding replacement term is a different domain term substring comprising a description of what the identified domain term contains. 11. The computer program product of claim 10, wherein the different domain term substring identifies an entity canonical name, an entity type, a relation, or a negative relation. 12. (canceled) 13. The computer program product of claim 8, wherein the replacement method is identified from a set consisting of: replacing the given identified domain term with an empty string, replacing the given identified domain term with a different domain term substring, or not replacing the given identified domain term. 14. The computer program product of claim 8, wherein the computer readable program further causes the computing device to: train a plurality of text classifier machine learning models based on a plurality of replacement method maps; classify, by the text classifier component configured with each of the plurality of the trained text classifier machine learning models, a test set of input texts to form a plurality of sets of class determinations, wherein each set of class determinations corresponds to a respective one of the plurality of replacement method maps; determine an accuracy of each of the plurality sets of class determinations; and configuring the domain terms replacer component with the replacement method map corresponding to a set of class determinations having a highest accuracy. 15. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to implement a domain terms selector component, a domain terms replacer component, and a text classifier trainer component, wherein the instructions cause the processor to: receive, by the data processing system, a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier; identify, by the domain terms selector component executing on a processor of the data processing system, at least one domain term in the texts of the training set; replace, by the domain terms replacer component executing on a processor of the data processing system, the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs; and train, by the text classifier trainer component executing on a processor of the data processing system, the text classifier machine learning model using the revised set to form a trained text classifier machine learning model, wherein replacing the identified domain term comprises, for each given identified domain term, determining a domain term type of the given identified domain term and replacing the given identified domain term based on selecting a replacement method associated with the domain term type by a replacement method map. 16. The apparatus of claim 15, wherein for at least one identified domain term, the corresponding replacement term is an empty string. 17. The apparatus of claim 15, wherein for at least one identified domain term, the corresponding replacement term is a different domain term substring comprising a description of what the identified domain term contains. 18. (canceled) 19. The apparatus of claim 15, wherein the replacement method is identified from a set consisting of: replacing the given identified domain term with an empty string, replacing the given identified domain term with a different domain term substring, or not replacing the given identified domain term. 20. The apparatus of claim 15, wherein the instructions further cause the processor to: train a plurality of text classifier machine learning models based on a plurality of replacement method maps; classify, by the text classifier component configured with each of the plurality of the trained text classifier machine learning models, a test set of input texts to form a plurality of sets of class determinations, wherein each set of class determinations corresponds to a respective one of the plurality of replacement method maps; determine an accuracy of each of the plurality sets of class determinations; and configuring the domain terms replacer component with the replacement method map corresponding to a set of class determinations having a highest accuracy. 21. The apparatus of claim 17, wherein the different domain term substring identifies an entity canonical name, an entity type, a relation, or a negative relation.