Abstract: Mechanisms for identifying hidden meaning in a portion of natural language content are provided. A primary portion of natural language content is received and a secondary portion of natural language content is identified that references the natural language content. The secondary portion of natural language content is analyzed to identify indications of meaning directed to elements of the primary portion of natural language content. A probabilistic model is generated based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content. A hidden meaning statement data structure is generated for the primary portion of natural language content based on the probabilistic model.

Abstract: At least one financial information for a user is received. A machine learning model is determined. The machine learning model is determined based on the user. A personality of the user is determined based on the machine learning model and the at least one financial information for the user. A recommendation is provided based on the determined personality of the user.

Abstract: In an approach to spatio-temporal visualization of the evolution of a topic, a computing device receives textual data from one or more sources. The computing device determines, based on semantic analysis, one or more topics of interest associated with the textual data, the location criteria, and the time criteria, wherein semantic analysis comprises merging related topics and splitting unrelated topics. The computing device generates one or more user-navigable visualizations comprising one or more graphs representing occurrences of a topic of interest in a designated geographic area and spanning a designated time window. The computing device displays the one or more user-navigable visualizations and one or more selector tools for designating a geographic area and a time window.

Abstract: A computer-implemented method includes receiving a question set. The question set is for a question answer system. The method receives a plurality of domain cluster topics. The plurality of domain cluster topics are for the question set. The method organizes the question set based on the plurality of domain cluster topics to yield a plurality of question set topics. The method receives a document corpus. The document corpus is for the question answer system. The method organizes the document corpus based on the plurality of domain cluster topics to yield a plurality of document corpus topics. The method maps the plurality of question set topics to the plurality of document corpus topics. The method determines whether any of the plurality of question set topics are unmatched. The method is responsive to any of the plurality of question set topics being unmatched. The method requests enrichment for the document corpus.

Abstract: A question is received. The question is in a natural language. The question is mapped to a data mining model. A query associated with the question is determined. The query is related to the data mining model. The query is executed on a dataset of structure data.

Abstract: Mechanisms for identifying hidden meaning in a portion of natural language content are provided. A primary portion of natural language content is received and a secondary portion of natural language content is identified that references the natural language content. The secondary portion of natural language content is analyzed to identify indications of meaning directed to elements of the primary portion of natural language content. A probabilistic model is generated based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content. A hidden meaning statement data structure is generated for the primary portion of natural language content based on the probabilistic model.

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 method, computer program product, and computer system for identifying data associated with an event. A recommendation is provided to at least the event based upon, at least in part, at least one of a character of the event determined based upon, at least in part, the data associated with the event, and a personality of a real-time crowd at the event determined based upon, at least in part, the data associated with the event.

Abstract: A method, computer program product, and computer system for identifying data associated with an event. A recommendation is provided to at least the event based upon, at least in part, at least one of a character of the event determined based upon, at least in part, the data associated with the event, and a personality of a real-time crowd at the event determined based upon, at least in part, the data associated with the event.

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: 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: Mechanisms are provided for generating a lexical resource for linguistic analysis. A document data structure is received that comprises a renaming directive and filter logic is applied to the document data structure to identify the renaming directive within the document data structure. The renaming directive is analyzed to identify a relationship between semantic concepts represented by the renaming directive that are to be used to update a lexical resource based on the renaming directive. The lexical resource is updated based on results of analyzing the renaming directive. The updated lexical resource is output to a linguistic analysis system which performs linguistic analysis of a portion of textual content based on the updated lexical resource.

Abstract: Embodiments of the invention broadly contemplate systems, methods, apparatuses and program products providing a faceted search tool enabling a user to enter a natural language query that contains representative terms the user wants to search for, and a convenient interface display in which the facets which contain the searched values are automatically resolved and presented.

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: Techniques, systems, and articles of manufacture for leveraging information for use in a traffic prediction scenario. A method includes extracting transportation-related information pertaining to a geographic region from multiple user-driven information systems, processing said transportation-related information by merging the transportation-related information across the multiple user-driven information systems and one or more transportation-related databases, and determining a supply and demand estimation for one or more aspects of transportation in the geographic region based on said processing of said transportation-related information.

Abstract: Embodiments of the present invention enable users to generate a summary for a document with respect to a concept, making use of inherent hierarchies present in a text document based on subject-object relationships of the sentences in the text document. In one embodiment, a text document is parsed into sentences, and a tuple is created for each sentence, the tuple comprising a subject and an object found in the sentence. The tuples may then be searched for a specified topic to identify matching tuples, as well as tuples that are related to the matching tuples based on relationships between their respective subjects and objects. A summary focused on the specified topic may then be generated using the sentences corresponding to the matching tuples and the tuples related to the matching tuples.

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.