Abstract: Embodiments relate to an intelligent computer platform to provide a contextual analogy response. The aspect of providing a contextual analogy response includes denoting an analogy phrase within a communication. An anaphora within the analogy phrase is detected and a set of sentences are parsed into grammatical components wherein the grammatical type for each parsed component is identified. A sentence is created with the detected anaphora and an action term from the analogy phrase. The set of sentences and the analogy phrase are matched with the identified components that are assigned to a solved analogy association. A related contextual response is attached to the solved analogy association to generate the outputted analogy response.

Abstract: In automatically modifying message signatures, a system extract message content from the message text from a sender to a recipient. Using at least the message content, the system determines: a relationship context capturing a nature of the relationship between the sender and the recipient, a temporal context capturing temporal features of the message content, a historical context capturing a nature of electronic interactions between the sender and the recipient over time, and a tone category capturing a primary tone and strength of the primary tone of the message content. The system selects a signature template associated with contextual data that includes the relationship, temporal, and historical contexts and the tone category. The signature template includes a set of sections where a signature is modified by inserting section values associated with the contextual data into the set of sections. The modified signature is inserted into the message text.

Abstract: Embodiments relate to an intelligent computer platform to receive a request for processing against a corpus. The request is analyzed and a lexical answer type (LAT), a first concept relevant to the received request and a second concept related to the identified first concept, are each identified. The LAT, together with the first and second concepts are utilized to create a first and second cluster. Documents are selectively populated into the clusters based on the respective LAT and concept qualifiers. The clusters are subject to sorting based on relevancy to the received request.

Abstract: Embodiments relate to an intelligent computer platform to support natural language (NL) processing. The request is analyzed and a lexical answer type (LAT) related to the received request is identified. A knowledge graph (KG) related to the LAT is identified and leveraged to extract a first concept related to the LAT and a second concept related to the first concept. First and second cluster are created, with the first cluster having the LAT and first concept as qualifiers, and the second cluster having the first and second concepts as qualifiers. Each of the formed clusters is populated with one or more documents. An inter-cluster assessment is conducted based on the relevancy of the populated document(s) to the received input. In addition, a machine learning model (MLM) corresponding to the KG is identified and utilized to selectively augment the MLM with the LAT, first and second concepts, and a corresponding relationship to the inter-cluster assessment.

Abstract: A method for creating content includes making a corpus of images available, adding a plurality of tag data to each of the images, receiving a query, extracting a trigger from the query, identifying a set of the images in the corpus, wherein identified images have tag data matching the extracted trigger, and creating a video comprising the identified images.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: Mechanisms are provided that generate a knowledge data structure for a medical condition. The mechanisms parse a natural language positional statement data structure representing a natural language positional statement corresponding to a medical condition, where the natural language positional statement specifies guidance information and patient attributes indicative of patients for which an action may be performed for the medical condition. The mechanisms extract the patient attributes and a grading value associated with the natural language positional statement. The mechanisms generate at least one weight value associated with each of the patient attributes based on the grading value.

Abstract: A method for de-identifying protected health information (PHI) associated with electronic medical records (EMRs) based on a common analysis structure (CAS) is provided. The method may include detecting a system event associated with a system comprising the EMRs. The method may further include in response to detecting the system event, detecting a first CAS associated with the EMRs. The method may further include extracting first CAS data associated with the first CAS, wherein the first CAS data comprises unstructured data associated with the EMRs and normalized annotations based on CAS objects that are associated with the unstructured data. The method may further include obfuscating the unstructured data associated with the first CAS. The method may also include generating a second CAS comprising the obfuscated unstructured data and a copied version of the normalized annotations, wherein the copied version of normalized annotations are correlated with the obfuscated unstructured data.

Abstract: A phrase that includes a trigger word that modifies a meaning within the phrase is received. The trigger word is identified. The words of the phrase that are modified by the trigger word are identified by analyzing features of the phrase that link the trigger word to other words. The phrase is interpreted by modifying the second subset of words according to the modification of the trigger word.

Abstract: A method, system and computer-usable medium are disclosed for adjusting fact-based answers provided by a question/answer (QA) system. A user submits a question to the QA system, where it is categorized into a question type. The QA system then processes the question to generate an answer. The QA system then generates an answer adjustment if it is determined that the question type and answer meet a predicted undesirable outcome. The answer adjustment may include a warning, a disclaimer, a recommendation, an alternative fact-based answer, a referral to an assistance service, or any combination thereof.

Abstract: A method, system and computer-usable medium are disclosed for computing file system management. A corpus of content is processed to extract metadata associated with folders and files referenced by a directory structure. Natural Language Processing (NLP) operations are then performed on the corpus to generate concept and entity data associated with each folder and file, followed by performing Natural Language (NL) classification operations to generate intent classification data, which in turn is processed to determine ranked, dominant intents for each folder and file. The corpus content, extracted metadata, concept and entity data, and ranked dominant intents are then processed to generate indexed content and term data. Application context data associated with an interaction is collected and processed to determine a user intent, which is then processed with the indexed content and term data to identify a corresponding folder and file, which in turn are provided to the user.

Abstract: Minimizing data security risks may be provided. A number and type of confidential data in a computing environment may be determined to generate a metric for the type of confidential data in the computing environment. The metric of the type of confidential data may be compared to a predetermined metric for the type. Responsive to determining the metric for the type of confidential data exceeding a predetermined metric for the type, an action may be performed to prevent more entries of the type of confidential data in the computing environment.

Abstract: Embodiments include method, systems and computer program products for redundantly storing product data for an electronic component on an external device. Aspects include determining that product data stored in a primary storage device is corrupted and determining whether the product data is available on the external device that is in communication with the electronic component. Based on a determination that the product data stored in the primary storage device is corrupted and that the product data is available on the external device, aspects include obtaining product data from the external device to continue operation of the electronic component.

Abstract: A method identifies and removes bias from a machine learning model. A user/computer inputs a plurality of input training data into a machine learning system to generate an output of labeled output data. The user/computer evaluates the labeled output data according to a consistency metric to associate the labeled output data with a corresponding consistency assessment. The user/computer selects each labeled output data having a consistency assessment indicating a consistency assessment that is greater than a predetermined threshold to form a labeled output data subset, and then creates additional labeling for the labeled output data subset. The user/computer utilizes the additional labeling to distinguish each labeled training data from labeled output data subset as being mislabeled and biased, and then adjusts the learning machine based on the labeled output data subset being mislabeled and biased.

Abstract: Mechanisms are provided for resolving annotation conflicts in a portion of content. A portion of content is received and annotation operations are performed on it using a plurality of annotators. Each annotator in the plurality of annotators generates a set of annotations for the portion of content, thereby generating a plurality of sets of annotations for the portion of content. The plurality of sets of annotations are analyzed to identify a conflict between a first annotation and a second annotation in the plurality of sets of annotations. The conflict between the first annotation and the second annotation is resolved to generate a resultant set of annotations for the portion of content. The resultant set of annotations is stored in an annotations data structure associated with the portion of content.

Abstract: A classifier receives a document and analyzes the document to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: A method for ground truth generation includes providing training questions to a machine learning system executing on a computer. The machine learning system generates candidate answers to each of the training questions. The method also includes providing the candidate answers to a plurality of subject matter experts for evaluation with respect to the training questions, wherein the evaluation comprises assignment of an SME relevance score to each of the candidate answers. The method further includes analyzing each of the candidate answers with respect to a plurality of scoring features, wherein each of the scoring features is indicative of quality of the candidate answer. The method yet further includes generating a ground truth metric value that indicates a measure of agreement between the subject matter experts relative to a measure of agreement between results of the analyzing.

Abstract: Mechanisms are provided that generate a knowledge data structure for a medical condition. The mechanisms parse a natural language positional statement data structure representing a natural language positional statement corresponding to a medical condition, where the natural language positional statement specifies guidance information and patient attributes indicative of patients for which an action may be performed for the medical condition. The mechanisms extract the patient attributes and a grading value associated with the natural language positional statement. The mechanisms generate at least one weight value associated with each of the patient attributes based on the grading value.

Abstract: A computer-implemented method according to one embodiment includes identifying a summary of a single instance of content, monitoring user interaction with the summary, and determining a relevancy of the summary to the single instance of content, based on the user interaction.