Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A method and/or system for query expansion may include: providing a set of training data in a given domain in the form of training question texts and training answer texts, identifying disjoint answer words in the training answer text that do not occur in the associated training question text, generating a graph of question word nodes and answer word nodes generated from the set of training data for the given domain in the form of the training question texts and the training answer texts, wherein edges are provided between a disjoint pair of a question word node for a question word in a training question and an answer word node for a disjoint answer word in an associated training answer, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Abstract: Methods, systems and computer program products are provided. Terms of unsubmitted input data entered by a user during composition of submitted input data for the performance of a task are identified. For an identified unsubmitted term, a relevance score indicating the relevance of the unsubmitted term to user intent is determined. The relevance score of the unsubmitted term is determined using a graphical semantic model based on the submitted input data as a representation of user intent. The identified unsubmitted term is selected, for use in supplementing the submitted input data for the performance of the task, based on the determined relevance score.

Abstract: Socially provided context-based messaging can include receiving a message content in a messaging system from a user, wherein the content includes objects; matching one or more objects of the message content to socially provided context information, which the socially provided context information is information relevant to an object obtained from a social network to which the user belongs; and displaying the socially provided context information with the message content for a recipient user to receive.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.

Abstract: Methods, systems and computer program products are provided. Terms of unsubmitted input data entered by a user during composition of submitted input data for the performance of a task are identified. For an identified unsubmitted term, a relevance score indicating the relevance of the unsubmitted term to user intent is determined. The relevance score of the unsubmitted term is determined using a graphical semantic model based on the submitted input data as a representation of user intent. The identified unsubmitted term is selected, for use in supplementing the submitted input data for the performance of the task, based on the determined relevance score.

Abstract: A method and associated computer system and computer program product for monitoring the use of language of a patient that may enable the identification, analysis and reporting on of the natural progression of various neurological or speech disorders, is provided. The method includes obtaining a verbal response of the patient to a stimulus, converting the verbal response into text and the generating of verbal and textual response data by analyzing the verbal response and response text, respectively. A response vector may be formed base on the verbal and textual response data, which may then be used to determine a reply to the patient. Improved accuracy and consistency of monitoring the use of language associated with neurological and speech disorders is achieved, leading to a greater rate of success in the treatment of these conditions.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, including providing weightings for the nodes and edges based on frequency data; and receiving a user query input, activating input nodes in the graph for words in the user query input, and applying spreading activation through the graph using the weightings to result in a top n most highly activated nodes that are used as candidate words for expansion of the user query input.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: Socially provided context-based messaging can include receiving a message content in a messaging system from a user, wherein the content includes objects; matching one or more objects of the message content to socially provided context information, which the socially provided context information is information relevant to an object obtained from a social network to which the user belongs; and displaying the socially provided context information with the message content for a recipient user to receive.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: Socially provided context-based messaging can include receiving a message content in a messaging system from a user, wherein the content includes objects; matching one or more objects of the message content to socially provided context information, which the socially provided context information is information relevant to an object obtained from a social network to which the user belongs; and displaying the socially provided context information with the message content for a recipient user to receive.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, including providing weightings for the nodes and edges based on frequency data; and receiving a user query input, activating input nodes in the graph for words in the user query input, and applying spreading activation through the graph using the weightings to result in a top n most highly activated nodes that are used as candidate words for expansion of the user query input.

Abstract: A mechanism is provided for semantic graph augmentation for domain adaptation. A semantic graph is provided based on a knowledge base in which concepts are linked by semantic relationships. A domain corpus of content in a new target domain is analyzed for adaptation with reference to one or more resources that provide syntactic analysis of the domain content that links portions of the domain corpus to existing concepts and links in the semantic graph. Triples are extracted from the analysis of the domain corpus of any two concepts that share a linking syntactic relationship. The triples are evaluated as potential relationship assertions. A relationship assertion is selected for addition as a new link between existing concepts in the semantic graph.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.

Abstract: A mechanism is provided in a question answering system for categorizing questions. The mechanism clusters documents from a corpus of documents to form a nested structure of clusters of documents. The mechanism records questions answered by question answering system in a database in association with answers returned and answer confidence values. The mechanism maps the questions to the nested structure of clusters of documents to form a nested structure of clusters of questions. The mechanism presents recommended questions based on the nested structure of clusters of questions in a user interface of the question answering system.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.