Abstract: A method, in a data processing system comprising a processor and a memory, for analyzing product impact, the method comprising receiving, by the data processing system, data representative of an entity's operations, receiving, by the data processing system, a selection of one or more products, parsing, by the data processing system, features of the one or more products from product data input, and generating, by the data processing system, product profiles for the one or more products based on the parsed features. The method further comprising generating, by the data processing system, risk assessment data based on the data representative of the entity's operations and the product profiles, the risk assessment data including an indication of one or more changes in risk metrics of one or more risk components associated with the entity's operations by offering the one or more products.

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: One or more portions of a text corpus indicative of operational risk from a set of risk assessment documents is identified. Contextual features from the one or more portions of the text corpus are determined by applying a natural language processing (NLP) algorithm on the one or more portions. Risk identifiers are extracted based on the determined contextual features. A risk assessment software is generated based on the extracted risk identifiers and an operational risk category.

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: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Mechanisms for identifying hidden meaning in a portion of natural language content are provided. A primary portion of natural language content is received and a secondary portion of natural language content is identified that references the natural language content. The secondary portion of natural language content is analyzed to identify indications of meaning directed to elements of the primary portion of natural language content. A probabilistic model is generated based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content. A hidden meaning statement data structure is generated for the primary portion of natural language content based on the probabilistic model.

Abstract: A computer-implemented method for creating question-answer pairs is provided. The computer-implemented method includes leveraging domain specific resources including, at least one or more of lexicons, glossaries, or knowledge bases for constructing templates for creating the question-answer pairs. The computer implemented method further includes leveraging user experiences of a plurality of users for constructing templates. The computer implemented method further includes eliminating erroneous question-answer pairs based on templates specifications of a heuristic process of the constructed templates.

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: An approach is provided to receive, at a question answering (QA) system, a question and identify a politeness corresponding to a number of terms corresponding to the question that are included in a corpus of the QA system. The approach identifies the politeness of one or more terms included in each of a set of candidate answers responsive to the question. Finally, the approach scores each of the candidate answers, with the scoring being based, in part, on the politeness identified for each of the terms.

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.

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: 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 knowledge graph is built based on a corpus stored in the computer system. The corpus includes a set of events and each event includes a respective set of entities. A first set of entities is identified in the NLQ. The first set of entities is used to identify a first set of significant events in the selected corpus in a first search depth. A second set of entities is identified in the first set of significant events. The knowledge graph determines which ones of the second set of entities are related to the entities in the first set of entities to produce a filtered second set of entities. The filtered second set of entities is used to identify a second set of significant events in the selected corpus in a second search depth. Members of the first and second set of significant events are presented to a user.