Abstract: A method, system and computer-usable medium are disclosed for preserving temporal relevance of content within a corpus. A corpus is processed to generate temporally-relevant metadata and mined content, which in turn are processed to generate first temporal relevancy metrics. The cache history of a web browser is likewise processed to generate second temporal relevancy metrics, which in turn is processed with the first temporal relevancy metrics to generate first temporal relevancy scores. New documents are ingested into the corpus and existing documents are revised. Temporally-relevant metadata and mined content associated with the updated corpus are then processed to generate third temporal relevancy metrics. The second and third temporal relevancy metrics are then processed to generate second temporal relevancy scores, which is then used to provide a temporally-relevant response to a query.

Abstract: A method, system and computer-usable medium are disclosed for preserving temporal relevance in a response to a query. A query is processed to extract first temporal features, which is then used to identify first documents within a corpus. The first documents are processed to generate first metadata and mined content, which is processed with the first temporal features to generate second documents having second temporal features corresponding to the first temporal features. The corpus is updated with new documents and revisions to the first documents to generate second documents. In turn, the second documents are processed to generate second metadata and mined content, which is then used to generate a ranked list of temporally-relevant documents.

Abstract: A computer-implemented method includes creating a classifier by: training a machine learning model using two or more tasks, wherein the tasks lie in two or more domains; including in the machine learning model at least one attribute common to at least two of said two or more domains; including in the machine learning model at least one latent feature that affects at least two of the two or more tasks that fall within one of the at least two domains; and constructing the classifier based on said machine learning model. The computer-implemented method further includes applying the classifier to at least one operational task.

Abstract: A computer-implemented method includes creating a classifier by: training a machine learning model using two or more tasks, wherein the tasks lie in two or more domains; including in the machine learning model at least one attribute common to at least two of said two or more domains; including in the machine learning model at least one latent feature that affects at least two of the two or more tasks that fall within one of the at least two domains; and constructing the classifier based on said machine learning model. The computer-implemented method further includes applying the classifier to at least one operational task.

Abstract: A natural language query (NLQ) is translated to a structured data query (e.g., a SQL statement) by extracting entities from the NLQ and replacing them with generic variables to form a generic query. The generic query is associated with a structured question type which includes structured data variables using natural language classifiers (NLCs). Specific data is inserted in the structured question type in relation to the structured data variables based on the extracted entities to form the structured data query. An ensemble of NLCs trained with different ground truths can be used to yield multiple candidate question types. One of the candidate question types is selected based on confidence levels. The multiple NLCs can include an NLC which is optimized according to a focus of the generic query. For example, an NLC can be optimized for a specific data structure (such as SQL), or for comparative queries.

Abstract: An approach is provided that improves a question answering (QA) computer system by automatically generating relationship templates. Event patterns are extracted from data in a corpus utilized by the QA computer system. The extracted event patterns are analyzed with the analysis resulting in a number of clusters of related event patterns. Relationship templates are then created from the plurality of clusters of related event patterns and these relationship templates are then utilized to visually interact with the corpus.

Abstract: An approach is provided that improves a question answering (QA) computer system by automatically generating relationship templates. Event patterns are extracted from data in a corpus utilized by the QA computer system. The extracted event patterns are analyzed with the analysis resulting in a number of clusters of related event patterns. Relationship templates are then created from the plurality of clusters of related event patterns and these relationship templates are then utilized to visually interact with the corpus.

Abstract: Answer effectiveness evaluations include providing, by a computing device, an answer to a search query received from a user, and in response to receiving a subsequent search query from the user, determining by the computing device a level of effectiveness of the answer to the search query with respect to the user. The determination includes comparing aspects of the search query to aspects of the subsequent search query, calculating, based on the comparing, a relevance score that indicates a measure of similarity between the aspects of the search query and the aspects of the subsequent search query, and determining that the answer effectively answers the search query when the relevance score exceeds a threshold value.

Abstract: Embodiments relate to digital image processing for diagnosis of a subject. More specifically, the embodiments relate to automation of diagnoses through data interpretation. An image is acquired from the subject. Elements are recognized within the image based on morphological features. The image is compared to learned data. Based on the comparison, a probability of a potential diagnosis(es) is calculated. A diagnosis of the subject is determined based on the potential diagnosis(es) and the calculated probability. The diagnosis may be changed based on a new image acquired from the subject.

Abstract: A natural language query (NLQ) is translated to a structured data query (e.g., a SQL statement) by extracting entities from the NLQ and replacing them with generic variables to form a generic query. The generic query is associated with a structured question type which includes structured data variables using natural language classifiers (NLCs). Specific data is inserted in the structured question type in relation to the structured data variables based on the extracted entities to form the structured data query. An ensemble of NLCs trained with different ground truths can be used to yield multiple candidate question types. One of the candidate question types is selected based on confidence levels. The multiple NLCs can include an NLC which is optimized according to a focus of the generic query. For example, an NLC can be optimized for a specific data structure (such as SQL), or for comparative queries.

Abstract: A system, method, and computer-readable medium are disclosed for identifying paraphrases in a natural language processing (NLP) system comprising: receiving a first phrase and a second phrase by a system; analyzing the first phrase and the second phrase to provide a semantic and structural hierarchical comparison assessment, the semantic and structural hierarchical comparison assessment having an associated semantic and structural hierarchical comparison assessment value; and determining whether the semantic and structural hierarchical comparison assessment value exceeds a predetermined paraphrase equivalency criteria; and, responsive to determining the semantic and structural hierarchical comparison assessment value exceeds the predetermined paraphrase equivalency criteria, classifying the second phrase as being a rewording of the first phrase.

Abstract: A method is disclosed for identifying paraphrases in a natural language processing (NLP) system comprising: receiving a first phrase and a second phrase by a system; analyzing the first phrase and the second phrase to provide a semantic and structural hierarchical comparison assessment, the semantic and structural hierarchical comparison assessment having an associated semantic and structural hierarchical comparison assessment value; and determining whether the semantic and structural hierarchical comparison assessment value exceeds a predetermined paraphrase equivalency criteria; and, responsive to determining the semantic and structural hierarchical comparison assessment value exceeds the predetermined paraphrase equivalency criteria, classifying the second phrase as being a rewording of the first phrase.

Abstract: Embodiments relate to digital image processing for diagnosis of a subject. More specifically, the embodiments relate to automation of diagnoses through data interpretation. An image is acquired from the subject. Elements are recognized within the image based on morphological features. The image is compared to learned data. Based on the comparison, a probability of a potential diagnosis(es) is calculated. A diagnosis of the subject is determined based on the potential diagnosis(es) and the calculated probability. The diagnosis may be changed based on a new image acquired from the subject.

Abstract: An approach is provided that improves a question answering (QA) computer system by automatically generating relationship templates. Event patterns are extracted from data in a corpus utilized by the QA computer system. The extracted event patterns are analyzed with the analysis resulting in a number of clusters of related event patterns. Relationship templates are then created from the plurality of clusters of related event patterns and these relationship templates are then utilized to visually interact with the corpus.

Abstract: An approach is provided that improves a question answering (QA) computer system by automatically generating relationship templates. Event patterns are extracted from data in a corpus utilized by the QA computer system. The extracted event patterns are analyzed with the analysis resulting in a number of clusters of related event patterns. Relationship templates are then created from the plurality of clusters of related event patterns and these relationship templates are then utilized to visually interact with the corpus.

Abstract: A method is disclosed for identifying paraphrases in a natural language processing (NLP) system comprising: receiving a first phrase and a second phrase by a system; analyzing the first phrase and the second phrase to provide a semantic and structural hierarchical comparison assessment, the semantic and structural hierarchical comparison assessment having an associated semantic and structural hierarchical comparison assessment value; and determining whether the semantic and structural hierarchical comparison assessment value exceeds a predetermined paraphrase equivalency criteria; and, responsive to determining the semantic and structural hierarchical comparison assessment value exceeds the predetermined paraphrase equivalency criteria, classifying the second phrase as being a rewording of the first phrase.

Abstract: A system, method, and computer-readable medium are disclosed for identifying paraphrases in a natural language processing (NLP) system comprising: receiving a first phrase and a second phrase by a system; analyzing the first phrase and the second phrase to provide a semantic and structural hierarchical comparison assessment, the semantic and structural hierarchical comparison assessment having an associated semantic and structural hierarchical comparison assessment value; and determining whether the semantic and structural hierarchical comparison assessment value exceeds a predetermined paraphrase equivalency criteria; and, responsive to determining the semantic and structural hierarchical comparison assessment value exceeds the predetermined paraphrase equivalency criteria, classifying the second phrase as being a rewording of the first phrase.

Abstract: Embodiments relate to digital image processing for diagnosis of a subject. More specifically, the embodiments relate to automation of diagnoses through data interpretation. An image is acquired from the subject. Elements are recognized within the image based on morphological features. The image is compared to learned data. Based on the comparison, a probability of a potential diagnosis(es) is calculated. A diagnosis of the subject is determined based on the potential diagnosis(es) and the calculated probability. The diagnosis may be changed based on a new image acquired from the subject.

Abstract: Embodiments relate to digital image processing for diagnosis of a subject. More specifically, the embodiments relate to automation of diagnoses through data interpretation. An image is acquired from the subject. Elements are recognized within the image based on morphological features. The image is compared to learned data. Based on the comparison, a probability of a potential diagnosis(es) is calculated. A diagnosis of the subject is determined based on the potential diagnosis(es) and the calculated probability. The diagnosis may be changed based on a new image acquired from the subject.

Abstract: A system includes a question answering system executed by a computer, a processor, and a memory coupled to the processor. The memory is encoded with instructions that when executed cause the processor to provide training for training the question answering system. The training system is configured to receive a first phrase and a second phrase, the first and second phrases being paraphrases of each other, convert the first phrase into a first logical form and the second phrase into a second logical form, generate a phrasal edit that includes a difference between the first logical form and the second logical form, convert the phrasal edit into a disjunctive logical form in two directions, and generate a first plurality of paraphrases of the first and second phrases based on the disjunctive logical form.