Abstract: A method for automatically annotating unstructured computer content associated with computer resources with additional contextual information from structured computer data sources is provided. The method may include, automatically identifying data elements within the unstructured computer content and matching extraction templates to the data elements. The method may further include automatically extracting an entity from the data elements using the extraction templates. The method may further include querying the structured computer data sources using the extracted entity to identify a data record in the structured computer data sources matching the entity. The method may further include extracting data from the data record and generating natural language text using the extracted data. The method may further include automatically annotating the unstructured computer content with the additional contextual information by inserting the generated natural language text into the unstructured computer content.

Abstract: In an approach for optimizing project and team success, prior to a user initiating a project, a processor creates a digital profile. A processor sets up the digital profile with a set of data gathered from one or more project related documents for a set of previous projects of the user. Responsive to the user initiating the project, a processor identifies one or more documents related to the project. A processor analyzes the one or more documents related to the project. A processor predicts an outcome of the project using machine learning, wherein the outcome of the project is a set of measures indicating a success factor, a quality factor, and a risk factor of the project. A processor generates an optimization suggestion, wherein the optimization suggestion is a suggestion to adjust one or more parameters of the project. A processor outputs the optimization suggestion to the user.

Abstract: A computer-implemented process for facilitating generation of a user interface design. The computer-implemented method provides for one or more processors to identify one or more elements added to a screen design by a first designer. The one or more processors predict one or more next elements of the screen design, based on the identified one or more elements added to the screen design and a learning model trained by machine learning techniques using a convolutional neural network. The one or more processors present the one or more next design elements as a recommendation to the first designer. The one or more processors determine a selection decision made by the first designer of the one or more next elements from the recommendation, and the one or more processors update learning model, based on selection decisions made by the first designer.

Abstract: Provided are a method, system, and computer program product in which operations are performed to receive a question that includes a descriptor and an indication that indicates that a unique answer to the question is expected. A determination is made of instances of matching descriptors and descriptor targets from a set of documents. The determined descriptor targets are compared for consistency. In response to determining that the determined descriptor targets are inconsistent, more restrictive descriptors are iteratively generated via a selection model based on metadata associated with the question until the descriptor targets are consistent. An answer to the question is returned from consistent descriptor targets.

Abstract: A computer device receives a request to search a corpus of documents for an entity, wherein the request includes a non-name identifier of the entity. The computing device identifies entries of text within the corpus of documents that reference the non-name identifier. The computing device applies natural language processing (NLP) to content associated with the identified entries within the corpus of documents, wherein the NLP identifies candidate entities associated with the non-name identifier. The computing device selects an entity from the candidate entities based, at least in part, on distances between the candidate entities and references to the non-name identifier in the identified entries. The computing device returns the selected entity to a submitter of the request.

Abstract: The method provides for receiving a plurality of documents including mentions of a target entity from a search query about the entity. The mentions of the target entity are identified in respective documents of the plurality of documents. Content surrounding the one or more mentions of the target entity are extracted with the mentions within the respective documents and form section. A respective document includes a plurality of sections. Metrics of relevance and irrelevance to the target entity are determined within the plurality of sections of the respective documents. A density score is generated for the plurality of sections of the respective documents. A relevancy score is assigned to respective documents of the plurality of documents, based on the density scores of the sections of the respective documents. The documents are ranked based on the relevancy score and presented in an order based on the ranking.

Abstract: Embodiments relate to an intelligent computer platform to selectively amend one or more document elements. A first document is subjected to natural language processing (NLP) and two or more document characteristics are subjected to an assessment to produce a characteristic value. The document characteristics and corresponding characteristic values are analyzed to produce a characteristic profile for each identified document characteristic. Upon receipt of a new document, document characteristic data and corresponding characteristic value(s) are identified. The corresponding characteristic value(s) of the new document is applied against the produced characteristic profile. New document characteristic data is selectively amended responsive to the comparison, and a new document version is created from the selective amendment.

Abstract: A mechanism is provided in a data processing system to implement a cognitive natural language processing (NLP) system with descriptor uniqueness identification to support named entity mention clustering. The mechanism annotates a set of documents from a corpus of documents for entity types and mentions, collects descriptor usages from all documents in the corpus of documents, analyzes the descriptor usages to classify the descriptors as base terms or modifier terms, generates compatibility scores for the descriptors, and performs entity merging of entity clusters based on the compatibility scores.

Abstract: Approaches presented herein enable presenting, as an initial response to a search query from a user, a set of analytic summaries prior to initiating a document security protocol in a secured document environment. Each analytic summary represents a set or cluster of documents. A summary relevance value is calculated for each analytic summary and a document relevance value is calculated for each individual document. Analytic summaries are presented to the user while each document within each analytic summary are prioritized for a security review based on its related summary relevance value and document relevance value. Based on the priorities, a security review is initiated for the documents so that a highly relevant document within an analytic summary undergoes the security review before a less relevant document. Access to a particular document is granted to the user according to the security review of the document.

Abstract: A system and a method for determining user synonyms in a query processing system is disclosed. A first query is received from a user of the query processing system. The query is processed through the query processing system to generate results for the first query. The user then provides a second query. The system determines a contextual similarity between the first query and the second query. When the first query and the second query are determined to be contextually similar, a first term is identified in the first query that is different from a second term in the second query. Once identified, the system determines if the first term and the second term are not listed as synonyms in the thesaurus. If they are not listed as synonyms they can be added as synonyms to the thesaurus.

Abstract: An embodiment for extracting information from semi-structured text is provided. The embodiment may include identifying one or more high confidence alignments of one or more entities and identifiers in a set of documents. The embodiment may also include analyzing one or more blocks of semi-structured text containing the one or more entities and identifiers. The embodiment may further include identifying one or more known alignments in each of the one or more blocks of semi-structured text. The embodiment may also include generating a structure template. The embodiment may further include applying the structure template to each of the one or more blocks of semi-structured text. The embodiment may also include annotating the set of documents with metadata reflecting the structure template and a location of each of the one or more blocks of semi-structured text.

Abstract: An interaction involving two or more users is identified based on at least one biometric value of a biometric attribute of one user of the two or more users exceeding a threshold value of the biometric attribute. A determination is made that the identified interaction is a stressful interaction for the one user. At least one de-escalation recommendation meant to reduce the intensity of the determined stressful interaction is transmitted to the one user.

Abstract: Embodiments relate to an intelligent computer platform to selectively amend one or more tokens in a document. A first document set is subjected to natural language processing (NLP) and a vector score is identified for two or more documents of the first document set. Upon receipt of a new document, the new document is subjected to NLP and a new document vector score is identified. The new document is analyzed against the first document set, and the identified vector score of the first document set is compared to the vector score of the new document. One or more tokens of the new document are amended responsive to the comparison, and a new document version is created from the selective amendment.

Abstract: A method, system and computer-usable medium are disclosed to identify a set of entity names based on a partial name of the entity utilizing discovered relationships. A partial name from a user is received as to the entity in order to retrieve a plurality of names of the entity in a corpus which can be a body or works, document, etc. References to the entries containing the partial name are retrieved from the corpus. A natural language processing is applied to content associated with references to identify candidate entities. A similarity is performed as to the identified candidate entities to form a similarity assessment, and from the candidate entities a selection is made based on a merging criteria.

Abstract: An embodiment for extracting information from semi-structured text is provided. The embodiment may include identifying one or more high confidence alignments of one or more entities and identifiers in a set of documents. The embodiment may also include analyzing one or more blocks of semi-structured text containing the one or more entities and identifiers. The embodiment may further include identifying one or more known alignments in each of the one or more blocks of semi-structured text. The embodiment may also include generating a structure template. The embodiment may further include applying the structure template to each of the one or more blocks of semi-structured text. The embodiment may also include annotating the set of documents with metadata reflecting the structure template and a location of each of the one or more blocks of semi-structured text.

Abstract: A computer device receives a request to search a corpus of documents for an entity, wherein the request includes a non-name identifier of the entity. The computing device identifies entries of text within the corpus of documents that reference the non-name identifier. The computing device applies natural language processing (NLP) to content associated with the identified entries within the corpus of documents, wherein the NLP identifies candidate entities associated with the non-name identifier. The computing device selects an entity from the candidate entities based, at least in part, on distances between the candidate entities and references to the non-name identifier in the identified entries. The computing device returns the selected entity to a submitter of the request.

Abstract: The present invention may receive the question, a plurality of the candidate answers, and a plurality of documents associated with the plurality of candidate answers in the natural language. Then the present invention may tokenize the question, the plurality of the candidate answers, and the plurality of the documents into a corresponding n-gram sequence. The present invention may map n-gram elements from the tokenized question to the n-gram elements of the plurality of the tokenized candidate answers and the plurality of the tokenized documents using the latent token representation technique. The present invention may score the plurality of tokenized candidate answers based on the latent token representation technique. Then, the present invention may determine the precise answer based on the plurality of scored candidate answers.

Abstract: Query service receives a query comprising at least a name component. The query service searches a document corpus to identify multiple passages, each comprising a mention of the name component within a selection of one or more documents of the document corpus. The query service collects bins, each bin comprising a distinct selection of the passages from the one or more documents, each of the bins identifying a separate relationship the name component participates in within the distinct selection of passages. The query service assesses a separate score of each respective bin reflecting the relevance of each respective bin to the query. The query service returns a response to the query with the bins each ranked according to each separate score.

Abstract: Provided are a method, system, and computer program product in which operations are performed to receive a question that includes a descriptor and an indication that indicates that a unique answer to the question is expected. A determination is made of instances of matching descriptors and descriptor targets from a set of documents. The determined descriptor targets are compared for consistency. In response to determining that the determined descriptor targets are inconsistent, more restrictive descriptors are iteratively generated via a selection model based on metadata associated with the question until the descriptor targets are consistent. An answer to the question is returned from consistent descriptor targets.

Abstract: A method, system and computer program product are provided for implementing an interface for rapid ground truth binning. A set of documents are received wherein each document has at least one entity in a set of entities. A user interface is provided for each received document allowing a user to view passages and select options related to confirming or denying an equivalence between the entity in the received document and an output document entity bin including the entity. Responsive to the user utilizing the user interface and confirming the equivalence, combining the received document with the output document entity bin with reference to the entity.