Abstract: Software that selects portions of unlabeled text for labeling, by performing the following operations: (i) receiving a set of unlabeled input text for classification with respect to a particular domain, wherein the domain includes a labeled corpus for which topics of a set of topics correspond to labels from the corpus, and wherein the topics include statistical probability distributions of words in the corpus; (ii) performing topic modeling on the input text to associate portions of the input text with respective classifications, wherein the classifications include statistical probability distributions of topics of the set of topics in the respective portions of the input text; and (iii) applying a machine learning-based selection strategy to the portions of the input text and their respective classifications to identify one or more portions of the input text for labeling.

Abstract: For a particular search query that is received by a host system, a preliminary set of search results is generated. The host system maps the query to one or more topics that are representative to the query. The host system provides topical click data associated with the topic to a machine-learning module that determines a relevancy score of the result utilizing the topical click data. The host system re-ranks the order of the result within a set of results based upon the relevancy score.

Abstract: A data handling system includes at least two host devices. For a particular search query that is received by a first host device, a preliminary set of search results is therein generated. The first host system maps the query to one or more topics that are representative to the query. The first host system provides topical click data associated with the topic to a machine-learning module located within a second host device that determines a relevancy score of the result utilizing the topical click data. The first host system obtains the relevancy score of the result and re-ranks the order of the result within a set of results based upon the relevancy score.

Abstract: Software that selects portions of unlabeled text for labeling, by performing the following operations: (i) receiving a set of unlabeled input text for classification with respect to a particular domain, wherein the domain includes a labeled corpus for which topics of a set of topics correspond to labels from the corpus, and wherein the topics include statistical probability distributions of words in the corpus; (ii) performing topic modeling on the input text to associate portions of the input text with respective classifications, wherein the classifications include statistical probability distributions of topics of the set of topics in the respective portions of the input text; and (iii) applying a machine learning-based selection strategy to the portions of the input text and their respective classifications to identify one or more portions of the input text for labeling.

Abstract: A data handling system includes at least two host devices. For a particular search query that is received by a first host device, a preliminary set of search results is therein generated. The first host system maps the query to one or more topics that are representative to the query. The first host system provides topical click data associated with the topic to a machine-learning module located within a second host device that determines a relevancy score of the result utilizing the topical click data. The first host system obtains the relevancy score of the result and re-ranks the order of the result within a set of results based upon the relevancy score.

Abstract: For a particular search query that is received by a host system, a preliminary set of search results is generated. The host system maps the query to one or more topics that are representative to the query. The host system provides topical click data associated with the topic to a machine-learning module that determines a relevancy score of the result utilizing the topical click data. The host system re-ranks the order of the result within a set of results based upon the relevancy score.

Abstract: A mechanism is provided in a data processing system to improve ground truth in a question answering cognitive system. The mechanism trains a similar passage machine learning model for a similar passage cognitive system using a question and answer key to form a trained similar passage machine learning model. The question and answer key comprises a list of question and answer specification pairs forming a ground truth for the question answering cognitive system. Each question is a text string and each answer specification references one or more text passages from a corpus of information.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and a memory comprising instructions which, when executed by the at least one processor, causes the at least one processor to train a similar passage cognitive system. The mechanism receives a question and answer key for a question answering cognitive system, the question and answer key comprising a list of question and answer specification pairs. Each question is a text string and each answer specification references one or more text passages from a corpus of information. The mechanism uses the question and answer key to generate a similar passage map for the similar passage cognitive system, the similar passage map comprising a list of text relation pairs. Each text relation pair comprises a sample input text component and a list comprising one or more sample output text components. The mechanism trains a similar passage machine learning model of the similar passage cognitive system using the similar passage map.