Abstract: Techniques for concept disambiguation for natural language processing are described herein. An aspect includes receiving a message from a user. Another aspect includes identifying an ambiguous concept in the message. Another aspect includes determining a plurality of concept candidates corresponding to the ambiguous concept. Another aspect includes determining, for each of the plurality of concept candidates, a respective similarity score based on user-specific concept metrics corresponding to the user. Another aspect includes ranking the plurality of concept candidates based on the respective similarity scores. Another aspect includes determining that the ambiguous concept corresponds to a top-ranked concept candidate of the ranked plurality of concept candidates.

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: In a frictionless handoff of audio content playing, a client device listens for ultrasonic audio. The client hears a playing of a modified audio content by another client device, which includes audio content and an ultrasonic audio quick response (QR) code overlaid on the audio content. The ultrasonic audio QR code includes location information corresponding to a location in the audio content. The client device extracts the ultrasonic audio QR code from the modified audio content. After determining that the playing of the modified audio content has stopped, the client device receives a command to resume playing of the audio content on the client device. In, response to the command, the client device retrieves location information in a last extracted ultrasonic audio QR code and plays the audio content starting at a location in the audio content corresponding to the location information in the last extracted ultrasonic audio QR code.

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: 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: 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: Software that performs the following operations: (i) receiving descriptive information associated with a domain expert; (ii) receiving a question and a corresponding candidate answer for the question; (iii) determining a set of scoring features to be used to evaluate the candidate answer, wherein the set of scoring features includes: at least one scoring feature pertaining to the question, at least one scoring feature pertaining to the candidate answer, and at least one scoring feature pertaining to the descriptive information; (iv) receiving a score from the domain expert, wherein the score is based, at least in part, on the domain expert's evaluation of the candidate answer; (v) generating a feature vector based on the set of scoring features; (vi) cross-correlating the feature vector with the score; and (vii) clustering the domain expert with one or more other domain experts according to the cross-correlation, thereby creating a first cluster.

Abstract: Examples described herein provide a computer-implemented method that includes receiving, by a processing device, the span of text, the span of text comprising a plurality of elements including at least an entity element and a temporal element. The method further includes organizing, by the processing device, the span of text as a natural language processing (NLP) parse tree. The method further includes traversing, by the processing device, the NLP parse tree by concatenating individual nodes of the span of text to generate the relation type between the entity element and the temporal element. The method further includes associating, by the processing device, the entity element, the relation type, and the temporal element together.

Abstract: Embodiments generally relate to the generation of a domain-specific phrasal dictionary. In some embodiments, a method includes receiving text from a user, wherein the text includes unstructured text of a natural language. The method further includes parsing the text into text chunks. The method further includes sending the text chunks to the user. The method further includes receiving one or more phrase categories and one or more predetermined phrases from the user, wherein each predetermined phrase of the one or more predetermined phrases corresponds to at least one phrase category of the one or more phrase categories. The method further includes comparing the predetermined phrases with the text chunks. The method further includes assigning at least one phrase category of the one or more phrase categories to at least one text chunk. The method further includes sending at least one text chunk and the at least one phrase category that is assigned to the at least one text chunk to the user.

Abstract: Embodiments of the present invention are directed to customizing annotations 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: Aspects of the invention include receiving a search query from a user computing device. Retrieving a set of passages based on the search query, wherein each passage contains passage evidence and an annotation embedded as metadata. Scoring each annotation and each passage evidence, where each annotation score is based on a feature vector of the annotation and the search query, and where each passage evidence score is based on a feature vector of the passage evidence and the search query. Ranking each passage based on a passage evidence score and a score of one annotation contained in the passage. Returning a ranked list of each passage to the user computing device.

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: 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 for concept disambiguation for natural language processing are described herein. An aspect includes receiving a message from a user. Another aspect includes identifying an ambiguous concept in the message. Another aspect includes determining a plurality of concept candidates corresponding to the ambiguous concept. Another aspect includes determining, for each of the plurality of concept candidates, a respective similarity score based on user-specific concept metrics corresponding to the user. Another aspect includes ranking the plurality of concept candidates based on the respective similarity scores. Another aspect includes determining that the ambiguous concept corresponds to a top-ranked concept candidate of the ranked plurality of concept candidates.

Abstract: Examples described herein provide a computer-implemented method that includes receiving a ground truth associated with a domain cartridge, the domain cartridge comprising a plurality of hierarchical layers. The method further includes analyzing annotation blocks in relation to data present in the ground truth to detect any errors in a set of natural language processing annotators. The analyzing includes computing a recall score, a precision score, and an F1 score for each annotation block in a lowest level layer of the plurality of hierarchical layers. The analyzing further includes determining whether an error is detected at the lowest level layer of the plurality of hierarchical layers based at least in part on the recall score, the precision score, and the F1 score. The analyzing further includes terminating the analyzing responsive to determining that the error is detected at the lowest level layer of the plurality of hierarchical layers.

Abstract: Aspects of the invention include systems and methods for the propagation of annotation metadata to overlapping annotations of a synonymous type. A non-limiting example computer-implemented method includes performing a comparison of a set of annotations to detect a subset of annotations that are candidates of being synonymous based on a first analysis. Whether a first annotation of the subset of annotations is synonymous with a second annotation of the subset of annotations is determined based on a second analysis. Distinct annotation metadata of the first annotation are cross-propogated with annotation metadata of the second annotation based on the second analysis.

Abstract: Techniques for document analysis using machine learning are provided. A selection of an index is received document, and a plurality of documents that refer to the index document is identified. For each respective document in the plurality of documents, a respective portion of the respective document is extracted, where the respective portion refers to the index document, and a respective vector representation is generated for the respective portion. A plurality of groupings is generated for the plurality of documents based on how each of the plurality of documents relate to the index document, by processing the vector representations using a trained classifier. Finally, at least an indication of the plurality of groupings is provided, along with the index document.

Abstract: Validating belief states of an artificial intelligence system includes providing a question answering service; detecting a negative sentiment of a user to an answer transmitted to a device associated with the user; and responsive to detecting the negative sentiment, detecting that the answer relates to a topic on which there is controversy. Next, a new belief state is added to the question answering service based on the controversy, and an updated answer is transmitted to the device, wherein the updated answer is based on the new belief state.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for modifying multimedia. The exemplary embodiments may include receiving a multimedia and one or more inputs, determining a required amount of modification to the multimedia based on the one or more inputs, generating a literary parse tree based on the multimedia, extracting one or more node features from one or more nodes of the parse tree, determining a node importance of the one or more nodes based on applying a model to the one or more node features, and modifying one or more portions of the multimedia corresponding to the one or more nodes based on the node importance and the required amount of multimedia modification.