Abstract: Scoring candidate evidence passages for criteria validation. Evidence data associated with a criteria, such that the evidence data entries include a decision indicator indicating that the criteria is either met or not met by the evidence data, is collected. Candidate evidences, making up a corpus of data associated with the criteria, against which the criteria is to be validated, are generated. Each candidate evidence is evaluated against the evidence data. A score indicating the validity of the criteria with respect to the candidate evidence is generated, based on the decision indicators associated with the evidence data entries.

Abstract: Scoring candidate evidence passages for criteria validation. Evidence data associated with a criteria, such that the evidence data entries include a decision indicator indicating that the criteria is either met or not met by the evidence data, is collected. Candidate evidences, making up a corpus of data associated with the criteria, against which the criteria is to be validated, are generated. Each candidate evidence is evaluated against the evidence data. A score indicating the validity of the criteria with respect to the candidate evidence is generated, based on the decision indicators associated with the evidence data entries.

Abstract: A method, computer system, and a computer program product for identifying sections in a document based on a plurality of visual features is provided. The present invention may include receiving a plurality of documents. The present invention may also include extracting a plurality of content blocks. The present invention may further include determining the plurality of visual features. The present invention may then include grouping the extracted plurality of content blocks into a plurality of categories. The present invention may also include generating a plurality of closeness scores for the plurality of categories by utilizing a Visual Similarity Measure. The present invention may further include generating a plurality of Association Matrices on the plurality of categories for each of the received plurality of documents based on the Visual Similarity Measure. The present invention may further include merging the plurality of categories into a plurality of clusters.

Abstract: A processor determines a set of terms frequently occurring within unstructured data associated with criteria of a query. The processor analyzes the unstructured data to match a term of the set of terms, to criteria of the query. Matching terms are retained and unmatched terms are checked for semantic similarity to terms of the criteria to determine an inferred match of the term of the unstructured data to terms of the criteria of the query. In response to determining an inferred match, the inferred match term is added to the second set of terms, and the processor compares the second set of terms to the terms of the criteria of the query, and removes matching terms, resulting in a third set of terms added to the set of criteria terms of the query. The additional query terms will enhance the recall without diluting the precision.

Abstract: Multi-turn conversation systems that are personalized to a user based on insights derived from big data are described. A computer-based conversation system for interacting with a user includes: a CPU, a computer readable memory, and a computer readable storage medium associated with a computer device; and program instructions defining a statement and question framer that is configured to: obtain insights about a user from a big data engine; and generate a response to an input from the user based on the insights and the input. The program instructions are stored on the computer readable storage medium for execution by the CPU via the computer readable memory.

Abstract: Multi-turn conversation systems that are personalized to a user based on insights derived from big data are described. A method includes: receiving, by a computer device, input from a user; obtaining, by the computer device, insights about the user; generating, by the computer device, a response based on the insights and the input; and outputting, by the computer device, the response.

Abstract: A processor determines a set of terms frequently occurring within unstructured data associated with criteria of a query. The processor analyzes the unstructured data to match a term of the set of terms, to criteria of the query. Matching terms are retained and unmatched terms are checked for semantic similarity to terms of the criteria to determine an inferred match of the term of the unstructured data to terms of the criteria of the query. In response to determining an inferred match, the inferred match term is added to the second set of terms, and the processor compares the second set of terms to the terms of the criteria of the query, and removes matching terms, resulting in a third set of terms added to the set of criteria terms of the query. The additional query terms will enhance the recall without diluting the precision.

Abstract: A method, computer system, and a computer program product for identifying sections in a document based on a plurality of visual features is provided. The present invention may include receiving a plurality of documents. The present invention may also include extracting a plurality of content blocks. The present invention may further include determining the plurality of visual features. The present invention may then include grouping the extracted plurality of content blocks into a plurality of categories. The present invention may also include generating a plurality of closeness scores for the plurality of categories by utilizing a Visual Similarity Measure. The present invention may further include generating a plurality of Association Matrices on the plurality of categories for each of the received plurality of documents based on the Visual Similarity Measure. The present invention may further include merging the plurality of categories into a plurality of clusters.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

Abstract: Scoring candidate evidence passages for criteria validation. Evidence data associated with a criteria, such that the evidence data entries include a decision indicator indicating that the criteria is either met or not met by the evidence data, is collected. Candidate evidences, making up a corpus of data associated with the criteria, against which the criteria is to be validated, are generated. Each candidate evidence is evaluated against the evidence data. A score indicating the validity of the criteria with respect to the candidate evidence is generated, based on the decision indicators associated with the evidence data entries.

Abstract: Scoring candidate evidence passages for criteria validation. Evidence data associated with a criteria, such that the evidence data entries include a decision indicator indicating that the criteria is either met or not met by the evidence data, is collected. Candidate evidences, making up a corpus of data associated with the criteria, against which the criteria is to be validated, are generated. Each candidate evidence is evaluated against the evidence data. A score indicating the validity of the criteria with respect to the candidate evidence is generated, based on the decision indicators associated with the evidence data entries.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

Abstract: In one embodiment of the present invention, one or more sections of a document are identified, and segments of text within the one or more sections are parsed. The parsed segments of text are analyzed to identify parsed segments of text associated with pointers indicative of example content. One or more links are generated between the identified parsed segments of text and one or more topics to which they pertain. Embodiments of the present invention can be used, for example, to increase accuracy of search results by identifying examples in documents returned as search results, as well as by filtering out examples that may cause the main content of text to be obscured in the search results.

Abstract: A processor determines a set of terms frequently occurring within unstructured data associated with criteria of a query. The processor analyzes the unstructured data to match a term of the set of terms, to criteria of the query. Matching terms are retained and unmatched terms are checked for semantic similarity to terms of the criteria to determine an inferred match of the term of the unstructured data to terms of the criteria of the query. In response to determining an inferred match, the inferred match term is added to the second set of terms, and the processor compares the second set of terms to the terms of the criteria of the query, and removes matching terms, resulting in a third set of terms added to the set of criteria terms of the query. The additional query terms will enhance the recall without diluting the precision.

Abstract: A processor determines a set of terms frequently occurring within unstructured data associated with criteria of a query. The processor analyzes the unstructured data to match a term of the set of terms, to criteria of the query. Matching terms are retained and unmatched terms are checked for semantic similarity to terms of the criteria to determine an inferred match of the term of the unstructured data to terms of the criteria of the query. In response to determining an inferred match, the inferred match term is added to the second set of terms, and the processor compares the second set of terms to the terms of the criteria of the query, and removes matching terms, resulting in a third set of terms added to the set of criteria terms of the query. The additional query terms will enhance the recall without diluting the precision.

Abstract: The present invention includes a method, system, and program product for detecting an event that includes receiving at least one data input stream from one or more sensors, selecting a data input stream from one of the one or more sensors, recording the data input stream on a recordable medium, specifying a rule comprising an event in the data input stream, and detecting at least one event in the data input stream based upon the rule.

Abstract: The present invention includes a method, system, and program product for detecting an event that includes receiving at least one data input stream from one or more sensors, selecting a data input stream from one of the one or more sensors, recording the data input stream on a recordable medium, specifying a rule comprising an event in the data input stream, and detecting at least one event in the data input stream based upon the rule.