Abstract: Software that uses machine-logic based algorithms to assess the value of idea descriptions by performing the following operations: (i) receiving an idea description pertaining to an idea; (ii) generating a density score for the idea, wherein the density score is based, at least in part, on one or more publication date densities of a set of works related to the idea description; (iii) generating a novelty score for the idea, wherein the novelty score is based, at least in part, on a comparison of the idea description to the set of works related to the idea description using natural language processing; and (iv) assessing the value of the idea based, at least in part, on the generated density score and the generated novelty score.

Abstract: Mechanisms are provided in a question answering (QA) system comprising a QA system pipeline that analyzes an input question and generates an answer to the input question, for pre-processing the input question. The mechanisms receive an input question and input the input question to a pre-processor flow path having one or more pre-processors. The one or more pre-processors transform the input question into a transformed question by correcting errors in a formulation of the input question that are determined to be detrimental to efficient and accurate processing of the input question by a QA system pipeline of the QA system. The transformed question is then input to the QA system pipeline of the QA system which processes the transformed question to generate and output an answer to the input question.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of the natural language content is generated by identifying latent logical terms within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value associated with at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. The propagating of the at least one knowledge value generates transaction records in a transaction knowledgebase data structure. A reasoning operation is executed based on the transaction knowledgebase data structure.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of the natural language content is generated by identifying latent logical terms within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value associated with at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. The propagating of the at least one knowledge value generates transaction records in a transaction knowledgebase data structure. A reasoning operation is executed based on the transaction knowledgebase data structure.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of the natural language content is generated by identifying latent logical terms within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value associated with at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. The propagating of the at least one knowledge value generates transaction records in a transaction knowledgebase data structure. A reasoning operation is executed based on the transaction knowledgebase data structure.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. Natural language content is received, upon which a reasoning operation is to be performed. A first parse representation of the natural language content is generated, by a parser, by performing natural language processing on the natural language content. A logical parse of the first parse is generated by identifying latent logical operators within the first parse indicative of logical relationships between elements of the natural language content. A reasoning operation on the logical parse is executed to generate a knowledge output indicative of knowledge associated with one or more of the logical relationships between elements of the natural language content.

Abstract: A natural language text is received from a user. The natural language text includes typing characteristics metadata. An emotive content of the natural language text is determined using a machine learning model. The natural language text is modified based on the emotive content.

Abstract: Software that uses machine-logic based algorithms to assess the value of idea descriptions by performing the following operations: (i) receiving an idea description pertaining to an idea; (ii) generating a density score for the idea, wherein the density score is based, at least in part, on one or more publication date densities of a set of works related to the idea description; (iii) generating a novelty score for the idea, wherein the novelty score is based, at least in part, on a comparison of the idea description to the set of works related to the idea description using natural language processing; and (iv) assessing the value of the idea based, at least in part, on the generated density score and the generated novelty score.

Abstract: A natural language text is received from a user. The natural language text includes typing characteristics metadata. An emotive content of the natural language text is determined using a machine learning model. The natural language text is modified based on the emotive content.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of a natural language content is generated by identifying latent logical operators within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value of at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. A reasoning operation is performed on the logical parse to generate a knowledge output indicative of knowledge associated with one or more of the logical relationships between elements of the natural language content.

Abstract: Mechanisms are provided for implementing a logical reasoning and justification engine that operates to receive a logical parse data structure of natural language content. The logical parse data structure comprises nodes and edges linking nodes and identifies latent logical terms within the natural language content indicative of logical relationships between elements of the natural language content. The engine further operates to receive a selection of a node in the logical parse data structure to thereby form a selected node, and execute at least one logical justification module on the selected node to identify zero or more justifying nodes that provide a contribution to a knowledge state of the selected node. The engine further operates to generate a logical justification output based on the identified zero or more justifying nodes, and output the logical justification output.

Abstract: Mechanisms are provided for processing natural language text. The natural language text is parsed to generate a logical parse which is then analyzed to identify a solicitation node of the logical parse for which a user is able to provide additional data to assist in processing the natural language text. A clarifying question is output to a computing device associated with a user based on content of the solicitation node and a response to the clarifying question is received from the user. The natural language text is then processed based on the response to the clarifying question received from the user to thereby generate a result. The result of the processing of the natural language text is output to a source of the natural language text.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. Natural language content is received, upon which a reasoning operation is to be performed. A first parse representation of the natural language content is generated, by a parser, by performing natural language processing on the natural language content. A logical parse of the first parse is generated by identifying latent logical operators within the first parse indicative of logical relationships between elements of the natural language content. A reasoning operation on the logical parse is executed to generate a knowledge output indicative of knowledge associated with one or more of the logical relationships between elements of the natural language content.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of the natural language content is generated by identifying latent logical terms within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value associated with at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. The propagating of the at least one knowledge value generates transaction records in a transaction knowledgebase data structure. A reasoning operation is executed based on the transaction knowledgebase data structure.

Abstract: Mechanisms are provided for processing logical relationships in natural language content. A logical parse of a first parse of a natural language content is generated by identifying latent logical operators within the first parse indicative of logical relationships between elements of the natural language content. The logical parse comprises nodes and edges linking nodes. At least one knowledge value is associated with each node in the logical parse. The at least one knowledge value of at least a subset of the nodes in the logical parse is propagated to one or more other nodes in the logical parse based on propagation rules. A reasoning operation is performed on the logical parse to generate a knowledge output indicative of knowledge associated with one or more of the logical relationships between elements of the natural language content.

Abstract: Mechanisms are provided in a question answering (QA) system comprising a QA system pipeline that analyzes an input question and generates an answer to the input question, for pre-processing the input question. The mechanisms receive an input question and input the input question to a pre-processor flow path having one or more pre-processors. The one or more pre-processors transform the input question into a transformed question by correcting errors in a formulation of the input question that are determined to be detrimental to efficient and accurate processing of the input question by a QA system pipeline of the QA system. The transformed question is then input to the QA system pipeline of the QA system which processes the transformed question to generate and output an answer to the input question.