Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Abstract: A mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

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 mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

Abstract: A mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.

Abstract: An approach is provided for an information handling system that includes a processor and a memory to generate a table of contents pertaining to a document. The approach semantically analyzes the document to identify semantic relationships of proximate elements of the document. A number of candidate headings corresponding to a semantically related section of the document are identified and each of the candidate headings are scored. Based on the scores of each of the candidate headings, a section heading for the semantically related section of the document is selected. The selected heading is then included in the table of contents for the section of the document. The process of identifying candidate headings, scoring candidates, and selecting the section heading is repeated for other semantically related sections of the document.

Abstract: A computing device receives one or more questions. The computing device determines at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions and determines at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions. The computing device determines an incentive based on the determined at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions, and the determined at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions.

Abstract: A computing device receives one or more questions. The computing device determines at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions and determines at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions. The computing device determines an incentive based on the determined at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions, and the determined at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions.

Abstract: A method, system and computer-usable medium are disclosed for identifying question-answer pair associations within a dialog. Natural language processing (NLP) is used to process a target dialog to identify questions and answers within the dialog. The relative positions of the identified questions and answers in the dialog are determined and then correlated to one another to generate question-answer pair associations. Sub-questions and their associated main questions are identified within the dialog, followed by the elimination of redundant question-answer pairs. Local cues contained in the dialog are then used in scoring and filtering processes to differentiate fact-based questions and answers from those that are opinionated.

Abstract: A method for extraction and enrichment of a procedure from a document is provided. The method may include identifying a potential location of a procedure in the document. The method may also include detecting a beginning boundary and an end boundary associated with the identified potential location of the procedure. The method may further include validating a text associated with the identified potential location of the procedure in the document. Additionally, the method may include determining an intent from the identified potential location of the procedure based on at least one of the beginning boundary, the end boundary, a surrounding text associated with the identified potential location of the procedure, a context associated with the document, and a title of the document. The method may also include enriching the procedure based on the determined intent.

Abstract: A mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

Abstract: Updating corpus indexes with derived analysis data including question data, answer data, and/or research data. The derived analysis data generated during question answering (QA) sessions of a QA system.

Abstract: A computer implemented method of generating a language section from tabular data in an electronic document may include identifying, in a first tabular portion of the electronic document, a set of categories used to organize tabular data. The method may include identifying a content characteristic for each category of the set of categories in the first tabular portion. And the method may include generating a first language section from at least two distinct categories of the set of categories, wherein a format of the first language section is based on the content characteristics for the at least two distinct categories.

Abstract: A computer implemented method of generating a language section from tabular data in an electronic document may include identifying, in a first tabular portion of the electronic document, a set of categories used to organize tabular data. The method may include identifying a content characteristic for each category of the set of categories in the first tabular portion. And the method may include generating a first language section from at least two distinct categories of the set of categories, wherein a format of the first language section is based on the content characteristics for the at least two distinct categories.

Abstract: A computer processor determines a first span of a communication, wherein a span includes content associated with one or more dialog statements. If the content of the first span contains one or more topic change indicators which are identified by at least one detector of a learning model, the computer processor, in response, generates scores for each of the one or more indicators. The computer processor aggregates scores of the one or more indicators of the first span, which may be weighted, to produce an aggregate score. The computer processor compares the aggregate score to a threshold value, wherein the threshold value is determined during training of the learning model, and the computer processor, in response to the aggregate score crossing the threshold value, determines a topic change has occurred within the first span.