Abstract: A mechanism is provided in a data processing system to implement an annotator for annotating content using context-based surface forms. The mechanism receives a dictionary data structure of surface forms comprising a plurality of regular expressions and input content. The mechanism compares a given span of text in the input content to each regular expression in the dictionary data structure. Responsive to the given span of text matching a given regular expression, an annotator annotates the span of text with a content indicator corresponding to a content category-associated with the dictionary data structure. The mechanism performs a natural language processing operation on the input content based on results of the annotation.

Abstract: A mechanism is provided in a data processing system to implement an annotator for annotating content using context-based surface forms. The mechanism receives a dictionary data structure of surface forms comprising a plurality of regular expressions and input content. The mechanism compares a given span of text in the input content to each regular expression in the dictionary data structure. Responsive to the given span of text matching a given regular expression, an annotator annotates the span of text with a content indicator corresponding to a content category associated with the dictionary data structure. The mechanism performs a natural language processing operation on the input content based on results of the annotation.

Abstract: Aspects of the present disclosure include determining, by a processor, an ontology, the ontology comprising a plurality of ontological relationships, receiving, by the processor, a plurality of passages, determining, by the processor, a target set of co-occurring entities comprising a first entity and a second entity, determining a first passage in the plurality of passages that includes the first entity and the second entity, determining, from the ontology, a first ontological relationship between the first entity and the second entity, analyzing the first passage to determine a congruency score for the first ontological relationship, and generating a relationship annotation between the first entity and the second entity in the first passages based on the congruency score being within a threshold.

Abstract: Techniques for natural language processing based on user context include identifying a context of a user and responsive to receiving a request from the user intended for processing by a natural language processing (NLP) model, accounting for the context of the user in relation to the request. A result from the NLP model having accounted for the context of the user is provided.

Abstract: Embodiments include methods, system and computer program products for extracting attributes from embedded table structures in a document. Aspects include identifying a table in the document and identifying one or more headers of the table by locating co-occurring attributes in the table. Aspects also include identifying a plurality of values in the table and creating an annotation for each of the plurality of values value in the table, wherein each annotation includes text extracted from the one or more headers that correspond to the location of the value in the table.

Abstract: Techniques for natural language processing based on user context include identifying a context of a user and responsive to receiving a request from the user intended for processing by a natural language processing (NLP) model, accounting for the context of the user in relation to the request. A result from the NLP model having accounted for the context of the user is provided.

Abstract: Aspects of the present disclosure include determining, by a processor, an ontology, the ontology comprising a plurality of ontological relationships, receiving, by the processor, a plurality of passages, determining, by the processor, a target set of co-occurring entities comprising a first entity and a second entity, determining a first passage in the plurality of passages that includes the first entity and the second entity, determining, from the ontology, a first ontological relationship between the first entity and the second entity, analyzing the first passage to determine a congruency score for the first ontological relationship, and generating a relationship annotation between the first entity and the second entity in the first passages based on the congruency score being within a threshold.

Abstract: Examples described herein provide a computer-implemented method that includes performing a text analysis on unstructured text to identify a restriction associated with a subject. The method further includes identifying an environmental requirement associated with the subject based at least in part on the restriction. The method further includes identifying one or more devices associated with the subject based at least in part on the environmental requirement. The method further includes receiving data from the one or more devices. The method further includes determining device usage information based at least in part on the data. The method further includes determining a compliance of the subject to the restriction based at least in part on the device usage information. The method further includes, responsive to determining non-compliance of the subject, changing a function of the one or more devices.

Abstract: Aspects of the present disclosure include receiving, by a processor, patient medical data associated with a patient, analyzing the patient medical data to generate a present patient summary for the patient, determining a cohort for the patient based on the present patient summary, the cohort comprising a plurality of similarly situated patients, and generating at least one medical prediction for the patient at a future age group based at least in part on the cohort for the patient.

Abstract: According to a computer-implemented method, an output is received resulting from natural language processing of unstructured text. In the output different phrases of the unstructured text are categorized into classes. A different visual distinction is generated for at least one class. A generated visual distinction is applied to phrases in the unstructured text based on their respective class. The unstructured text is displayed such that phrases in the unstructured text that correspond to the at least one class appear with the corresponding visual distinction indicative of that class.

Abstract: Aspects of the invention include converting text from a first image file into a first machine-encodable text, wherein the image file includes a first section of text that is offset from a second section of text. Analyzing the first image file to detect a position of the first section of text. Embedding a first section of the first machine encodable-text with metadata describing the position of the first section of text. Reformatting the first section of the first machine encodable-text to conform to the position of the first section of text.

Abstract: According to a computer-implemented method, a cognitive model container is created. The container includes a set of artifacts. Each artifact includes 1) content used by a cognitive service to convert unstructured text into structured text and 2) metadata. During deployment of a container, for each artifact a set of deployment descriptors are automatically identified. The deployment descriptors identify how the artifact is to be executed in the cognitive service. Also, during deployment of the container, content of an artifact is pushed to a number of cognitive services based on the deployment descriptors. The container is instantiated along with the set of artifacts to the cognitive service. During runtime execution of the container, content of each artifact in a container is obtained and unstructured text is converted into structured text based on the content of the artifacts.

Abstract: Methods, systems, and computer program products for auto-suggestion of expanded terms for concepts are provided. Aspects include analyzing, by a cognitive model, a seed dictionary to determine one or more concepts associated with the seed dictionary, determining a target ontology, analyzing, by the cognitive model, the target ontology to determine one or more expanded terms for each concept of the one or more concepts, determining, by the cognitive model, a confidence score for each of the one or more expanded terms, and updating the seed dictionary by associating the one or more expanded terms with a corresponding concept from the one or more concepts based at least in part on the confidence score exceeding a first threshold confidence score.

Abstract: An artifact identification engine identifies artifacts from structured and unstructured data in one or more documents based on pre-defined artifacts, by using cognitive annotations. The identified artifacts are analyzed based at least on received inputs. A cartridge that includes artifacts that are relevant to the structured and unstructured data is generated, based on the analyzing.

Abstract: A mechanism is provided in a data processing system to implement an annotator for annotating content using context-based surface forms. The mechanism receives a dictionary data structure of surface forms comprising a plurality of regular expressions and input content. The mechanism compares a given span of text in the input content to each regular expression in the dictionary data structure. Responsive to the given span of text matching a given regular expression, an annotator annotates the span of text with a content indicator corresponding to a content category associated with the dictionary data structure. The mechanism performs a natural language processing operation on the input content based on results of the annotation.

Abstract: Methods, systems, and computer program products for cognitive model modification are provided. Aspects include receiving a plurality of documents, receiving a cognitive model, identifying a set of cognitive model modification based on an alteration to the cognitive model, and for each cognitive model modification in the set of cognitive model modifications determining an updated concept or surface form based on each cognitive model modification, identifying one or more documents from the plurality of documents based on the updated concept or surface form, adding the one or more documents to a subset of documents, and generating an updated cognitive model based on one or more text analytics performed, by the cognitive model, on the subset of documents.

Abstract: Methods, systems, and computer software product are provided to receive a request to encode a prescription for a patient on a blockchain ledger. A request is received to encode patient pick-up information for the prescription on the blockchain ledger. Based on the patient pick-up information, whether a request to fill a prescription is valid is evaluated. The blockchain ledger is scanned within a window of time for patient patterns of behavior indicating possible prescription drug abuse by the patient. Also provided is computing a score for the patient, the score representing a likelihood of fraud or abuse by the patient. A disposition for the request to fill the prescription is determined, based on a consensus of voting peers, and the disposition is recorded on the blockchain ledger.

Abstract: An inference rules identification mechanism is provided for automatically identifying inference rules. The mechanism parses content of at least one natural language document in a collection of natural language documents utilizing natural language processing to identify a set of attributes and corresponding values present in the content of the at least one natural language document thereby forming a set of attribute/value pairs. For each attribute/value pair in the set of attribute/value pairs, the mechanism determines an affinity correspondence measure of the attribute/value pair with each other attribute/value pair in the set of attribute/value pairs. The mechanism determines, based on the affinity correspondence measures of each attribute/value pair with each other attribute/value pair in the set of attribute/value pairs, a set of inferred rules.

Abstract: Embodiments include methods, system and computer program products for extracting attributes from embedded table structures in a document. Aspects include identifying a table in the document and identifying one or more headers of the table by locating co-occurring attributes in the table. Aspects also include identifying a plurality of values in the table and creating an annotation for each of the plurality of values value in the table, wherein each annotation includes text extracted from the one or more headers that correspond to the location of the value in the table.

Abstract: An artifact identification engine identifies artifacts from structured and unstructured data in one or more documents based on pre-defined artifacts, by using cognitive annotations. The identified artifacts are analyzed based at least on received inputs. A cartridge that includes artifacts that are relevant to the structured and unstructured data is generated, based on the analyzing.