Abstract: Provided are a computer program product, system, and method for pre-processing a table in a document for natural language processing (NLP). A graphical user interface (GUI) provides a representation of table items in a table in a document including a set of a main element comprising an entity whose value is to be extracted, a conditional element that refines the entity, and a value element comprising a value for the entity. Graphical controls are rendered in the GUI to enable a user to select an element from the table to be the main element, conditional element, and value element. The set of the main element, conditional element, and value element are updated with the user selected element to form a modified set. The modified set of the main element, conditional element, and the value element are provided to an NLP engine to perform natural language processing.

Abstract: Provided are a computer program product, system, and method for pre-processing a table in a document for natural language processing. A table in a document is parsed to extract column headers, row headers, and data cells, which are processed to determine an initial set of a main element comprising an entity whose value is to be extracted, a conditional element that refines the entity, and a value element comprising a value for the entity. A user selection is received of at least one of the column headers, row headers, and data cells for at least one of the main element, conditional element, and the value element in the initial set to produce a modified set of the main element, conditional element, and value element. The modified set is provided to a natural language processing engine to perform natural language processing of the document including the table, using the modified set.

Abstract: Provided are a computer program product, system, and method for using domain specific vocabularies to spellcheck input strings. A determination is made of a determining a domain specific vocabulary comprising domain specific tokens for a user. The input strings in the input text from the user are processed to determine whether the input strings match the domain specific tokens in the domain specific vocabulary. Indication is made that input strings that are determined to match the domain specific tokens in the domain specific vocabulary are not to be spellchecked by a global vocabulary available to users across domains.

Abstract: Provided are a computer program product, system, and method for using domain specific vocabularies to spellcheck input strings. A determination is made of a determining a domain specific vocabulary comprising domain specific tokens for a user. The input strings in the input text from the user are processed to determine whether the input strings match the domain specific tokens in the domain specific vocabulary. Indication is made that input strings that are determined to match the domain specific tokens in the domain specific vocabulary are not to be spellchecked by a global vocabulary available to users across domains.

Abstract: Aspects of the invention include a computer-implemented method for generating promise identifiers for documents. Aspects include processing a document including a reference, wherein processing includes performing natural language processing (NLP) the document, and identifying the reference included in the document. Aspects also include generating a promise identifier for the reference in the document, and responsive to processing the document, resolving the promise identifier for the reference by providing data of the reference associated with the promise identifier. Aspects of the invention also include a computer program product and system for generating promise identifiers for documents.

Abstract: Provided are a computer program product, system, and method for pre-processing a table in a document for natural language processing. A table in a document is parsed to extract column headers, row headers, and data cells, which are processed to determine an initial set of a main element comprising an entity whose value is to be extracted, a conditional element that refines the entity, and a value element comprising a value for the entity. A user selection is received of at least one of the column headers, row headers, and data cells for at least one of the main element, conditional element, and the value element in the initial set to produce a modified set of the main element, conditional element, and value element. The modified set is provided to a natural language processing engine to perform natural language processing of the document including the table, using the modified set.

Abstract: Embodiments of the present invention are directed to evaluating the performance characteristics of annotator configurations against text pattern constructs in unstructured text. In a non-limiting embodiment of the invention, unstructured text is received by a processor. A text pattern construct is identified in the unstructured text and a first performance characteristic of an annotator is determined based on the text pattern construct. The text pattern construct is converted to a natural language text and a second performance characteristic of the annotator is determined based on the natural language text. A delta is determined between the first performance characteristic and the second performance characteristic. An alternative annotator configuration is identified for a portion of the unstructured text comprising the text pattern construct.

Abstract: Techniques include updating a semantic search function with a custom ontology, the semantic search function initially supporting a separate ontology having been used to enrich a corpus. The custom ontology is used to augment input of a search query for the semantic search function, thereby providing a custom user experience for searching the corpus.

Abstract: Provided are a computer program product, system, and method for pre-processing a table in a document for natural language processing. A table in a document is parsed to extract column headers, row headers, and data cells, which are processed to determine an initial set of a main element comprising an entity whose value is to be extracted, a conditional element that refines the entity, and a value element comprising a value for the entity. A user selection is received of at least one of the column headers, row headers, and data cells for at least one of the main element, conditional element, and the value element in the initial set to produce a modified set of the main element, conditional element, and value element. The modified set is provided to a natural language processing engine to perform natural language processing of the document including the table, using the modified set.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for resolving the intents of user expression. The exemplary embodiments may include receiving a user expression, receiving a first resolver having an input class and an output class based on the user expression, determining whether the first resolver can be resolved based on the user expression, and based on determining that the first resolver can be resolved based on the user expression, resolving the first resolver.

Abstract: Annotations are customized for a tenant-specific search within a public corpus. In a non-limiting embodiment of the invention, a cartridge file is received by a semantic search application. The cartridge file includes a new attribute definition that is not available in an index of the semantic search application. The new attribute definition is incorporated within the index based on an approximation of one or more existing attributes in the index. One or more documents are retrieved from the public corpus based on a concept search using the incorporated new attribute definition and the one or more documents are annotated based on the incorporated new attribute definition. The annotated one or more documents are stored in a tenant-specific dataset separate from the public corpus.

Abstract: Techniques for targeted partial re-enrichment include determining that at least one natural language processing (NLP) request is associated with at least one surface form, the NLP request being for a corpus, a database comprising preexisting annotations associated with the corpus. An index query related to the at least one surface form is performed to generate index query results, the index query results including identification of portions of the corpus affected by the NLP request. A scope of the NLP request related to the database is determined based on the index query results, the scope including identification of impacted candidate annotations of the preexisting annotations affected by the NLP request. An NLP service is performed on the corpus according to the scope and the portions, thereby resulting in updates. The updates are committed to the database associated with the corpus.

Abstract: Techniques include integrating a custom ontology into a semantic search function, the semantic search function being configured to perform a semantic search over a corpus enriched with a separate ontology. The semantic search function is executed using the custom ontology to perform the semantic search of the corpus. Results are generated from the semantic search of the corpus based on input received by the semantic search function.

Abstract: Aspects of the invention include resolving future reference identifiers for documents. Aspects of the invention include processing a document including a reference to a future event, wherein processing includes performing natural language processing (NLP) on the document, and identifying the reference to the future event included in the document. Aspects of the invention also include generating a future reference identifier for the reference to the future event, and responsive to processing an occurrence of the future event, resolving the future reference identifier by providing data from a subsequent document for the future event associated with the future reference identifier.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for determining the intents of user expression. The exemplary embodiments may include receiving a user expression, extracting one or more entities from the user expression, gathering one or more resolvers associated with the one or more entities, identifying a first resolver of the one or more resolvers based on the user expression and the one or more training expressions, and resolving the first resolver to generate a first output.

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: Technology for applying explainable artificial training algorithms (XAI) to training machine learning algorithms for identifying potentially developing computer vision syndrome (CVS), CVS and/or recommended remedial action(s) that a user can perform to counter potentially developing CVS and/or existing CVS. In some embodiments, the XAI includes a Contrastive Explainability model. In some embodiments, the training performed by the XAI includes assigning weight factors respectively to CVS input parameters (for example, blink rate) based upon how strong the respective CVS input factor is correlated with development of CVS in the user.

Abstract: Embodiments include cross-document propagation of entity metadata. Aspects include identifying a set of documents from a plurality of documents, the set of documents being related to one another and identifying a concept in a first document of the set of documents and creating an annotation corresponding to the concept. Aspects also include evaluating the annotation from the first document against all of the documents in the set of documents and identifying a concept match between the annotation and a mention discovered in a second document in the set of documents. Aspects further include creating a metadata linkage between the concept in the first document to the mention in the second document.

Abstract: Provided are a computer program product, system, and method for using domain specific vocabularies to spellcheck input strings. A determination is made of a determining a domain specific vocabulary comprising domain specific tokens for a user. The input strings in the input text from the user are processed to determine whether the input strings match the domain specific tokens in the domain specific vocabulary. Indication is made that input strings that are determined to match the domain specific tokens in the domain specific vocabulary are not to be spellchecked by a global vocabulary available to users across domains.

Abstract: Aspects of the present disclosure describe techniques for generating a machine learning model for extracting information from textual content. The method generally includes receiving a training data set including a plurality of documents having related textual strings. A relevancy model is generated from the training data set. The relevancy model is generally configured to generate relevance scores for a plurality of words extracted from the plurality of documents. A knowledge graph model illustrating relationships between the plurality of words extracted from the plurality of documents is generated from the training data set. The relevancy model and the knowledge graph model are aggregated into a complimentary model including a plurality of nodes from the knowledge graph model and weights associated with edges between connected nodes, wherein the weights comprise relevance scores generated from the relevancy model, and the complimentary model is deployed for use in analyzing documents.