Abstract: A semantic query over a corpus of data is constructed using a graphical user interface to create an aggregation of graphical representations of annotations associated with a plurality of data elements contained within the corpus of data and graphical representations of search terms contained within the plurality of data elements. The aggregation includes at least one annotation and at least one search term. The relative positions of the graphical representations of the annotations and the search terms are manipulated within the aggregation within the graphical user interface to express relationships among the annotations and search terms, yielding a visual spatial representation of the semantic query. The annotations, search terms and expressed relationships define the semantic query that is used to search the corpus of data.

Abstract: An approach to candidate answer generation by leveraging structural information in semi-structured resources, such as the title of a document and anchor texts in a document.

Abstract: A semantic query over a corpus of data is constructed using a graphical user interface to create an aggregation of graphical representations of annotations associated with a plurality of data elements contained within the corpus of data and graphical representations of search terms contained within the plurality of data elements. The aggregation includes at least one annotation and at least one search term. The relative positions of the graphical representations of the annotations and the search terms are manipulated within the aggregation within the graphical user interface to express relationships among the annotations and search terms, yielding a visual spatial representation of the semantic query. The annotations, search terms and expressed relationships define the semantic query that is used to search the corpus of data.

Abstract: A semantic query over a corpus of data is constructed using a graphical user interface to create an aggregation of graphical representations of annotations associated with a plurality of data elements contained within the corpus of data and graphical representations of search terms contained within the plurality of data elements. The aggregation includes at least one annotation and at least one search term. The relative positions of the graphical representations of the annotations and the search terms are manipulated within the aggregation within the graphical user interface to express relationships among the annotations and search terms, yielding a visual spatial representation of the semantic query. The annotations, search terms and expressed relationships define the semantic query that is used to search the corpus of data.

Abstract: A method and an apparatus for selecting an answer to a natural language question. The method includes: detecting a named entity in the natural language question; extracting information related to an answer from the natural language question; searching in linked data according to the detected named entity; generating a candidate answer according to a search result; parsing the candidate answer according to the information related to the answer; and obtaining a value of a feature of the candidate answer; and evaluating each candidate answer by synthesizing the value of the feature of the candidate answer.

Abstract: A method (and system) which autonomously generates a cohesive script from a text database for creating a speech corpus for concatenative text-to-speech, and more particularly, which generates cohesive scripts having fluency and natural prosody that can be used to generate compact text-to-speech recordings that cover a plurality of phonetic events.

Abstract: A semantic query over a corpus of data is constructed using a graphical user interface to create an aggregation of graphical representations of annotations associated with a plurality of data elements contained within the corpus of data and graphical representations of search terms contained within the plurality of data elements. The aggregation includes at least one annotation and at least one search term. The relative positions of the graphical representations of the annotations and the search terms are manipulated within the aggregation within the graphical user interface to express relationships among the annotations and search terms, yielding a visual spatial representation of the semantic query. The annotations, search terms and expressed relationships define the semantic query that is used to search the corpus of data.

Abstract: A method and an apparatus for selecting an answer to a natural language question. The method includes: detecting a named entity in the natural language question; extracting information related to an answer from the natural language question; searching in linked data according to the detected named entity; generating a candidate answer according to a search result; parsing the candidate answer according to the information related to the answer; and obtaining a value of a feature of the candidate answer; and evaluating each candidate answer by synthesizing the value of the feature of the candidate answer.

Abstract: A computer-implemented method (and system) of automatically generating a story, includes selecting a theme of the story, examining elements of the theme and instantiating the theme, and using the theme to select and control other aspects of the story.

Abstract: A computer-implemented method (and system) of automatically generating a story, includes selecting a theme of the story, examining elements of the theme and instantiating the theme, and using the theme to select and control other aspects of the story.

Abstract: A system and method for importing and reconciling component variables with container variables in a document, includes identifying variables in a component, for each of these variables determining if there is a variable in the container that refers to a same domain concept, and, if an identification occurs, associating the variable in the component with the variable in the container.

Abstract: A system (and method) for assembly process includes rendering document knowledge as textual components with variable fields, building an object-oriented domain model including domain knowledge, and linking the document knowledge to the domain knowledge.