Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method in a data processing system comprising a processor and a memory, the memory comprising instructions which cause the processor to be configured to implement a self-training engine of a question and answer system, the method comprising: pairing, by the self-training engine, an unanswered natural language question with portions of an electronic document to generate an unlabeled data set; training, by the self-training engine, a model based on a labeled data set comprising a finite number of question and answer pair data structures; applying, by the self-training engine, the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question; modifying, by the self-training engine, the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure; and training, by the self-training engine, the model based on the modified labeled data set. 2. The method of claim 1, wherein the electronic document is a patient electronic medical record (EMR), and wherein the portions are clinical notes in the patient EMR. 3. The method of claim 1, further comprising generating, for each portion of the electronic document paired with the unanswered natural language question in the unlabeled data set, a confidence value based on application of the model to the unlabeled data set. 4. The method of claim 3, further comprising: retrieving, for the unanswered natural language question in the unlabeled data set, a question profile specifying characteristics of a question in the labeled data set that corresponds to the unanswered natural language question; and dynamically generating a threshold confidence value for the unanswered natural language question based on the characteristics in the question profile. 5. The method of claim 4, wherein retrieving the question profile comprises: calculating, for each question in the labeled data set, a similarity score measuring a similarity of first features of the question to second features of the unanswered natural language question; selecting a question from the labeled data set having a highest similarity score; and retrieving a profile associated with the selected question as the question profile. 6. The method of claim 4, further comprising: comparing, for each of the portions of the electronic document paired with the unanswered natural language question, a confidence value of the portion to the dynamically generated threshold confidence value; and identifying the answer for the unanswered natural language question from a portion of the electronic document whose corresponding confidence value meets a requirement specified by the threshold confidence value. 7. The method of claim 6, wherein identifying the answer for the unanswered natural language question from a portion of the electronic document whose corresponding confidence value meets a requirement specified by the threshold confidence value comprises selecting a plurality of answers from a plurality of portions of the electronic document, wherein a number of answers in the plurality of answers is determined based on the characteristics in the question profile. 8. The method of claim 4, wherein the characteristics of the unanswered natural language question comprise at least one of key entities associated with the unanswered natural language question, relations between key entities associated with the unanswered natural language question, an answer type, or an indicator of whether the answer is likely to be found in a single portion or across multiple portions of an electronic document. 9. The method of claim 4, wherein the question profile further comprises an indicator of all other questions in the unlabeled data set and labeled data set that are similar to the unanswered natural language question, wherein similar questions are questions whose answers are the same answer. 10. The method of claim 4, further comprising: clustering questions in the unlabeled data set based on comparisons of features of the questions in the unlabeled data set with characteristics of the question profile to generate a question cluster comprising similar questions, wherein similar questions are questions whose answers are the same answer; and propagating a label associated with a question answer pair of a first question in the question cluster to all other questions in the question cluster. 11. A computer program product comprising a 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: pair an unanswered natural language question with portions of an electronic document to generate an unlabeled data set; train a model based on a labeled data set comprising a finite number of question and answer pair data structures; apply the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question; modify the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure; and train the model based on the modified labeled data set. 12. The computer program product of claim 11, wherein the electronic document is a patient electronic medical record (EMR), and wherein the portions are clinical notes in the patient EMR. 13. The computer program product of claim 11, wherein the computer readable program further causes the computing device to generate, for each portion of the electronic document paired with the unanswered natural language question in the unlabeled data set, a confidence value based on application of the model to the unlabeled data set. 14. The computer program product of claim 13, wherein the computer readable program further causes the computing device to: retrieve, for the unanswered natural language question in the unlabeled data set, a question profile specifying characteristics of a question in the labeled data set that corresponds to the unanswered natural language question; and dynamically generate a threshold confidence value for the unanswered natural language question based on the characteristics in the question profile. 15. The computer program product of claim 14, wherein the computer readable program further causes the computing device to retrieve the question profile at least by: calculating, for each question in the labeled data set, a similarity score measuring a similarity of first features of the question to second features of the unanswered natural language question; selecting a question from the labeled data set having a highest similarity score; and retrieving a profile associated with the selected question as the question profile. 16. The computer program product of claim 14, wherein the computer readable program further causes the computing device to: compare, for each of the portions of the electronic document paired with the unanswered natural language question, a confidence value of the portion to the dynamically generated threshold confidence value; and identify the answer for the unanswered natural language question from a portion of the electronic document whose corresponding confidence value meets a requirement specified by the threshold confidence value. 17. The computer program product of claim 16, wherein the computer readable program further causes the computing device to identify the answer for the unanswered natural language question from a portion of the electronic document whose corresponding confidence value meets a requirement specified by the threshold confidence value at least by selecting a plurality of answers from a plurality of portions of the electronic document, wherein a number of answers in the plurality of answers is determined based on the characteristics in the question profile. 18. The computer program product of claim 14, wherein the characteristics of the unanswered natural language question comprise at least one of key entities associated with the unanswered natural language question, relations between key entities associated with the unanswered natural language question, an answer type, or an indicator of whether the answer is likely to be found in a single portion or across multiple portions of an electronic document. 19. The computer program product of claim 14, wherein the question profile further comprises an indicator of all other questions in the unlabeled data set and labeled data set that are similar to the unanswered natural language question, wherein similar questions are questions whose answers are the same answer. 20. 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: pair an unanswered natural language question with portions of an electronic document to generate an unlabeled data set; train a model based on a labeled data set comprising a finite number of question and answer pair data structures; apply the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question; modify the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure; and train the model based on the modified labeled data set.