A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a ground truth generation system for quality assessment scoring of articles in a corpus. The ground truth generation system receives recommendations of a set of recommended articles from subject matter experts. The ground truth generation system identifies a set of non-recommended articles. A topic clustering component within the ground truth generation system performs topic clustering on a combination of the set of recommended articles and the set of non-recommended articles to form a set of topic clusters containing recommended articles and non-recommended articles. The ground truth generation system identifies a first number of recommended articles and a second number of non-recommended articles in each of the set of topic clusters to form a quality assessment training set. The mechanism trains a quality assessment machine learning model using the quality assessment training set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a computing device configured with instructions executing on a processor of the computing device to implement a ground truth generation system for quality assessment scoring of articles in a corpus, the method comprising: receiving, by the ground truth generation system, recommendations of a set of recommended articles from subject matter experts; identifying, by the ground truth generation system, a set of non-recommended articles; performing, by a topic clustering component within the ground truth generation system, topic clustering on a combination of the set of recommended articles and the set of non-recommended articles to form a set of topic clusters containing recommended articles and non-recommended articles; identifying, by the ground truth generation system, a first number of recommended articles and a second number of non-recommended articles in each of the set of topic clusters to form a quality assessment training set; and. training a quality assessment machine learning model using the quality assessment training set. 2. The method of claim 1, wherein receiving the recommendations comprises performing Web scraping of article recommendation Web sites. 3. The method of claim 1, wherein the set of recommended articles are in a set of journals and wherein identifying the set of non-recommended articles comprises identifying the set of non-recommended articles from the set of journals. 4. The method of claim 1, wherein the first number and the second number are equal in size. 5. The method of claim 1, wherein performing topic clustering comprises performing k-means clustering. 6. The method of claim 1, wherein identifying the second number of non-recommended articles comprises: for each article in the first number of recommended articles, randomly sampling an article that is in the same topic cluster, is not recommended, and has not already been included in the second number of non-recommended articles. 7. The method of claim 1, further comprising performing candidate answer scoring in a question answering system based on a quality assessment scorer and at least one other scorer in a question answering system pipeline of the question answering system. 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a ground truth generation system for quality assessment scoring of articles in a corpus, wherein the computer readable program causes the computing device to: receive, by the ground truth generation system, recommendations of a set of recommended articles from subject matter experts; identify, by the ground truth generation system, a set of non-recommended articles; perform, by a topic clustering component within the ground truth generation system, topic clustering on a combination of the set of recommended articles and the set of non-recommended articles to form a set of topic clusters containing recommended articles and non-recommended articles; identify, by the ground truth generation system, a first number of recommended articles and a second number of non-recommended articles in each of the set of topic clusters to form a quality assessment training set; and train a quality assessment machine learning model using the quality assessment training set. 9. The computer program product of claim 8, wherein receiving the recommendations comprises performing Web scraping of article recommendation Web sites. 10. The computer program product of claim 8, wherein the set of recommended articles are in a set of journals and wherein identifying the set of non-recommended articles comprises identifying the set of non-recommended articles from the set of journals. 11. The computer program product of claim 8, wherein the first number and the second number are equal in size. 12. The computer program product of claim 8, wherein performing topic clustering comprises performing k-means clustering. 13. The computer program product of claim 8, wherein identifying the second number of non-recommended articles comprises: for each article in the first number of recommended articles, randomly sampling an article that is in the same topic cluster, is not recommended, and has not already been included in the second number of non-recommended articles, 14. The computer program product of claim 8, wherein the computer readable program further causes the computing device to perform candidate answer scoring in a question answering system based on a quality assessment scorer and at least one other scorer in a question answering system pipeline of the question answering system. 15. A computing device comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a ground truth generation system for quality assessment scoring of articles in a corpus, wherein the instructions cause the processor to: receive, by the ground truth generation system, recommendations of a set of recommended articles from subject matter experts; identify, by the ground truth generation system, a set of non-recommended articles; perform, by a topic clustering component within the ground truth generation system, topic clustering on a combination of the set of recommended articles and the set of non-recommended articles to form a set of topic clusters containing recommended articles and non-recommended articles; identify, by the ground truth generation system, a first number of recommended articles and a second number of non-recommended articles in each of the set of topic clusters to form a quality assessment training set; and train a quality assessment machine learning model using the quality assessment training set. 16. The computing device of claim 15, wherein receiving the recommendations comprises performing Web scraping of article recommendation Web sites. 17. The computing device of claim 15, wherein the set of recommended articles are in a set of journals and wherein identifying the set of non-recommended articles comprises identifying the set of non-recommended articles from the set of journals. 18. The computing device of claim 15, wherein the first number and the second number are equal in size. 19. The computing device of claim 15, wherein identifying the second number of non-recommended articles comprises: for each article in the first number of recommended articles, randomly sampling an article that is in the same topic cluster, is not recommended, and has not already been included in the second number of non-recommended articles, 20. The computing device of claim 15, wherein the instructions further cause the processor to perform candidate answer scoring in a question answering system based on a quality assessment scorer and at least one other scorer in a question answering system pipeline of the question answering system.