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: 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.