Patent application number | Description | Published |
20150033230 | Virtual Machine Allocation at Physical Resources - Communications between virtual machines are monitored to identify virtual machines that have an affinity with each other, such as where the virtual machines have greater than a threshold of communication between each other. An affinity table tracks virtual machines having an affinity relationship and is referenced upon start-up or migration of a virtual machine so that a starting-up or migrating virtual machine will run on the same processing resource as virtual machines with which it has an affinity relationship. | 01-29-2015 |
20150142418 | Error Correction in Tables Using a Question and Answer System - Mechanisms are provided for performing tabular data correction in a document. The mechanisms receive a natural language document comprising a portion of content and analyze the portion of content within the natural language document to identify an erroneous sub-portion comprising an erroneous or missing item of information. The mechanisms generate a semantic signature for the erroneous sub-portion and generate a query based on the semantic signature. The mechanisms apply the query to a knowledge base to identify a candidate sub-portion of content. The mechanisms correct the erroneous sub-portion using the identified candidate sub-portion of content to generate a corrected natural language document. | 05-21-2015 |
20150179082 | Dynamic Identification and Validation of Test Questions from a Corpus - Mechanisms for automatically generating a set of test questions for use in generating a test to be administered to human test takers are provided. The mechanisms ingest a document from a corpus of documents to select a potential test question answer from the document and generate a candidate test question based on the potential test question answer. The mechanisms evaluate the candidate test question using a Question and Answer (QA) system to generate a result indicative of whether the candidate test question should be maintained for test generation. The mechanisms store the candidate test question in the set of test questions in response to a result indicating that the candidate test question should be maintained for test generation. | 06-25-2015 |
20150293901 | Utilizing Temporal Indicators to Weight Semantic Values - A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for utilizing temporal indicators to weight semantic values. A set of temporal characteristics is identified of a set of initial candidate answers. For each initial candidate answer in the set of initial candidate answers: a distance value is generated for each of the set of temporal characteristics of the set of initial candidate answers, a multiplier value is determined with which to weight an initial confidence score associated with the initial candidate answer using the distance value; a sentiment value is determined of the initial candidate answer, and a final weight value is determined using the multiplier value, the sentiment value, and the initial confidence score associated with the initial candidate answer. A set of temporally refined candidate answers is then provided using the determined final weight values. | 10-15-2015 |
20150293917 | Confidence Ranking of Answers Based on Temporal Semantics - A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics. Responsive to receiving an input question, a set of candidate answers is identified from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain. A confidence score is associated with each of the candidate answers and each confidence score associated with each candidate answer is refined based on a set of temporal characteristics identified in the input question. A set of temporally refined candidate answers is then provided to the user. | 10-15-2015 |
20150356420 | Rating Difficulty of Questions - A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score. | 12-10-2015 |
20160078018 | Method for Identifying Verifiable Statements in Text - A method, system and computer-usable medium are disclosed for identifying verifiable statements in a corpus of text. A training corpus of text containing manually annotated instances of verifiable and non-verifiable statements is processed to parse the text into segmented statements, which are in turn processed to extract features. The extracted features and the annotated statements are then processed with a machine learning algorithm to generate a verifiable statement classification model. In turn, the verifiable statement classification model is referenced by a verifiable statement classification system to distinguish verifiable and non-verifiable statements contained within an input corpus of text. | 03-17-2016 |
20160078349 | Method for Identifying Verifiable Statements in Text - A method, system and computer-usable medium are disclosed for identifying verifiable statements in a corpus of text. A training corpus of text containing manually annotated instances of verifiable and non-verifiable statements is processed to parse the text into segmented statements, which are in turn processed to extract features. The extracted features and the annotated statements are then processed with a machine learning algorithm to generate a verifiable statement classification model. In turn, the verifiable statement classification model is referenced by a verifiable statement classification system to distinguish verifiable and non-verifiable statements contained within an input corpus of text. | 03-17-2016 |
20160098379 | Preserving Conceptual Distance Within Unstructured Documents - A method, system and computer-usable medium are disclosed for preserving conceptual distance within unstructured documents by characterizing conceptual relationships. Natural language processing is applied to content in a plurality of documents to identify topics and subjects. Analytic analysis is then applied to the identified topics and subjects to identify concepts. The content in each of the plurality of documents is partitioned into a first structured hierarchy, preserving at least one structure in each document inherent in the each document. Access is then provided to the content through a first index based upon utilizing the first structured hierarchy and through a second index utilizing a second structured hierarchy. The conceptual relationship criteria are based upon a directed graph with weights based upon a similarity and a distance based upon concepts. | 04-07-2016 |
20160098398 | Method For Preserving Conceptual Distance Within Unstructured Documents - A method, system and computer-usable medium are disclosed for preserving conceptual distance within unstructured documents by characterizing conceptual relationships. Natural language processing is applied to content in a plurality of documents to identify topics and subjects. Analytic analysis is then applied to the identified topics and subjects to identify concepts. The content in each of the plurality of documents is partitioned into a first structured hierarchy, preserving at least one structure in each document inherent in the each document. Access is then provided to the content through a first index based upon utilizing the first structured hierarchy and through a second index utilizing a second structured hierarchy. The conceptual relationship criteria are based upon a directed graph with weights based upon a similarity and a distance based upon concepts. | 04-07-2016 |