Abstract: A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.

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: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

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 for adjusting device system settings in response to analysis of displayed content. In some embodiments, a processor may extract content from contents displayed on a screen of a device. The processor may analyze features of the content. The processor may classify the content as belonging to a content class, based on analysis of the content features, utilizing a machine learning model. The processor may update the device system settings based on the content class.

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: An embodiment for expanding a search query is provided. The embodiment may include receiving a stopping criterion for stopping a search. The embodiment may also include receiving an initial search query. The embodiment may further include submitting the initial search query to an information retrieval system. The embodiment may also include identifying enrichment terms from the retrieved initial set of documents. The embodiment may further include generating a subsequent search query that includes one or more enrichment terms from the retrieved initial set of documents. The embodiment may also include submitting the subsequent search query to the information retrieval system. The embodiment may further include determining whether the stopping criterion is met, and in response to determining the stopping criterion is not met, iterating identifying, generating, submitting steps until the stopping criterion is met.

Abstract: A controller generating a knowledge graph of entries, each entry comprising a separate entity identifier and a separate entity mention identifier within a separate document of a corpus of documents with a located relationship and one or more computed prefix-based geotemporal values determined from geotemporal information associated with the separate entity mention identifier within the separate document. The controller, in response to receiving an input comprising a particular entity and a threshold value, mapping the threshold value to a geospatial hash prefix type and a temporal hash prefix type. The controller applying geospatial hash prefix type and the temporal hash prefix type to the entries in the knowledge graph to determine a response to the input indicating one or more geotemporal proximate entities identified within a degree of geotemporal proximity to the particular entity set by the threshold value.

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: A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.

Abstract: Provided is a system, method, and computer program product for generating a three-dimensional (3D) printable file of a complete object by re-assembling pieces of a broken object using generative adversarial network techniques. A processor may generate a 3D scan of each piece of a plurality of pieces of a broken object. The processor may assemble the 3D scan of each piece of the plurality of pieces to generate a re-assembled object, where the re-assembled object includes one or more gaps. The processor may fill the one or more gaps in the re-assembled object to create a complete object. The processor may generate a 3D printable file of the complete object.

Abstract: An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Abstract: A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.

Abstract: A method learns approximate translations of unfamiliar measurement units during deep question answering (DeepQA) system training and usage. The DeepQA system receives a training set containing Question-Answer (QA) pairs having known unit-of-measurement terms, where each QA pair contains an answer having a known numeric value for a corresponding question from the QA pair. The DeepQA system receives a question from each QA pair from the training set to the DeepQA system in order to find answers and passage phrases to the question from each QA pair, and then identifies all found answers and passage phrases having values that are within a predetermined range of answer values of the training set, where one or more of the identified all found answers and passage phrases contain unfamiliar unit-of-measurement terms, in order to learn approximate translations of the unfamiliar unit-of-measurement terms.

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: In an approach for identifying troubled contracts using a health score, a processor receives a contract. A processor identifies a list of requirements of the contract using a first Natural Language Processing technique. A processor trains a model to recognize the list of requirements of the contract. A processor receives at least one deliverable document associated with the contract. A processor applies a plurality of health metrics to the at least one deliverable document associated with the contract to identify evidence of completion of each requirement of the list of requirements of the contract. A processor outputs the health score for each requirement of the list of requirements of the contract.

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: A method, system, and computer-usable medium are disclosed for answering general background questions on a topic from documents with glossary sections, A set of documents with glossaries is received from which a set of terms and associated glossary entries are extracted, where each term has a corresponding glossary entry. Association is performed of related glossary entries. The associations is based on a similarity algorithm to form glossary clusters where each glossary cluster refers to one or more glossary entries. A query with query terms tailored to general information is received. The glossary clusters are ranked relevance to the query terms to form a ranked set. A set of glossary clusters meeting a high ranked threshold is selected and provided.

Abstract: A controller, responsive to detecting geospatial and temporal information associated with a mention of an entity in a document, for converting the geospatial information into a specified geospatial format and the temporal information into a specified temporal format. The controller for computing prefix-based geospatial values for the converted geospatial information and prefix-based temporal values for the converted temporal information. The controller for encoding an entry in a knowledge graph for the mention of the entity from the document with the prefix-based geospatial values and the prefix-based temporal values, wherein each digit of the prefix-based geospatial values and the prefix-based temporal values in the knowledge graph that matches another one or more prefix-based geospatial values and another one or more prefix-based temporal values in another entry for another entity mention in the knowledge graph reflects a degree of granularity of geotemporal proximity of the entity and the another entity.

Abstract: Provided is a system, method, and computer program product for generating a three-dimensional (3D) printable file of a complete object by re-assembling pieces of a broken object using generative adversarial network techniques. A processor may generate a 3D scan of each piece of a plurality of pieces of a broken object. The processor may assemble the 3D scan of each piece of the plurality of pieces to generate a re-assembled object, where the re-assembled object includes one or more gaps. The processor may fill the one or more gaps in the re-assembled object to create a complete object. The processor may generate a 3D printable file of the complete object.