Abstract: A method, a processing device, and a computer program product are provided. At least one processing device correlates textual medical information related to the subject with characteristics of an image of a medical condition of the subject to generate a subject signature. The at least one processing device compares the subject signature with multiple reference signatures to determine at least one reference signature corresponding to the subject signature. Each reference signature is associated with a corresponding medical condition and is generated by correlating textual medical information regarding the corresponding medical condition with characteristics of an image of the corresponding medical condition. The at least one processing device identifies the medical condition of the subject based on the medical conditions associated with the determined at least one reference signature. Information is provided regarding the identified medical condition of the subject.

Abstract: Techniques disclosed herein describe generating annotations for text content rendered on a web browser. The web browser is configured with a content analytics plug-in configured to receive content to render in the web browser. The content itself includes unstructured text. The content analytics plug-in evaluates the unstructured text using content analytics and natural language processing (NLP) techniques to characterize the meaning of one or more passages in the unstructured text. The content analytics plug-in then generates annotations based on the characterizations to display when the web browser renders the content.

Abstract: Techniques disclosed herein describe generating annotations for text content rendered on a web browser. The web browser is configured with a content analytics plug-in configured to receive content to render in the web browser. The content itself includes unstructured text. The content analytics plug-in evaluates the unstructured text using content analytics and natural language processing (NLP) techniques to characterize the meaning of one or more passages in the unstructured text. The content analytics plug-in then generates annotations based on the characterizations to display when the web browser renders the content.

Abstract: A method, a processing device, and a computer program product are provided. Unstructured text may be analyzed to identify medical condition information of multiple occurrences of a medical condition for at least one subject. Times and geographic locations corresponding to the multiple occurrences of the medical condition may be obtained. Environmental information that corresponds to the times and the geographic locations of the multiple medical condition occurrences, may be retrieved. Correlations between the medical condition information and the retrieved environmental information for the at least one subject may be determined. Environmental factors affecting the medical condition, based on the determined correlations, are identified.

Abstract: A lexical annotator that identifies a chunk of a communication and an associated sentiment is created. In real time, while monitoring a communication from a user, the lexical annotator is used to identify the sentiment for the chunk of the communication, and the sentiment for the chunk of the communication is provided.

Abstract: Determining a subject type for an entity in a text segment. A text segment is selected, which includes one or more single-word or multi-word entities. Natural language processing is performed on the selected text segment to identify entities that constitute subjects of the selected text segment. One entity is selected. A variant annotation is associated with the selected entity. The variant annotation reflects multiple subject types for the selected entity and a value for each subject type. The most probable subject type is determined for the selected entity, based on a combination of natural language processing rules and dictionary listings. The value of the annotation is incremented for the subject type corresponding to the most probable subject type for the selected entity, so that the highest value of the annotation indicates the most probable subject type for the selected entity within the selected text segment.

Abstract: Provided are techniques for a domain specific representation of document text for accelerated natural language processing. A document is selected from a set of documents to be analyzed. A character stream from the document is converted into a token stream based on tokenization rules. Irrelevant tokens are removed from the token stream. The tokens remaining in the token stream are converted into an integer domain representation based on a domain specific ontology dictionary. The integer domain representation are stored to a Graphics Processing Unit (GPU) processing queue of each of one or more GPUs. Then, a result set is received from the one or more GPUs.

Abstract: Provided are techniques for a domain specific representation of document text for accelerated natural language processing. A document is selected from a set of documents to be analyzed. A character stream from the document is converted into a token stream based on tokenization rules. Irrelevant tokens are removed from the token stream. The tokens remaining in the token stream are converted into an integer domain representation based on a domain specific ontology dictionary. The integer domain representation are stored to a Graphics Processing Unit (GPU) processing queue of each of one or more GPUs. Then, a result set is received from the one or more GPUs.

Abstract: Provided is a technique for providing an interactive dashboard based on real-time sentiment analysis for synchronous communication. In real time, while monitoring communications between a first user and a second user, a cumulative sentiment score is generated representing a sentiment of the first user during a period of time and an instantaneous sentiment score representing a sentiment of the first user at a current time. In real time, an interactive dashboard is displayed with a visual representation for the first user having a first indicator representing the cumulative sentiment score and a second indicator representing the instantaneous sentiment score.

Abstract: In an approach for parallelizing document processing in an information handling system, a processor receives a document, wherein the document includes text content. A processor extracts information from the text content, utilizing natural language processing and semantic analysis, to form tokenized semantic partitions, comprising a plurality of sub-documents. A processor schedules a plurality of concurrently executing threads to process the plurality of sub-documents.

Abstract: In an approach for parallelizing document processing in an information handling system, a processor receives a document, wherein the document includes text content. A processor extracts information from the text content, utilizing natural language processing and semantic analysis, to form tokenized semantic partitions, comprising a plurality of sub-documents. A processor schedules a plurality of concurrently executing threads to process the plurality of sub-documents.

Abstract: Techniques disclosed herein describe generating annotations for text content rendered on a web browser. The web browser is configured with a content analytics plug-in configured to receive content to render in the web browser. The content itself includes unstructured text. The content analytics plug-in evaluates the unstructured text using content analytics and natural language processing (NLP) techniques to characterize the meaning of one or more passages in the unstructured text. The content analytics plug-in then generates annotations based on the characterizations to display when the web browser renders the content.

Abstract: Techniques disclosed herein describe generating annotations for text content rendered on a web browser. The web browser is configured with a content analytics plug-in configured to receive content to render in the web browser. The content itself includes unstructured text. The content analytics plug-in evaluates the unstructured text using content analytics and natural language processing (NLP) techniques to characterize the meaning of one or more passages in the unstructured text. The content analytics plug-in then generates annotations based on the characterizations to display when the web browser renders the content.

Abstract: A data structure is generated containing enumerators for data types of a domain, text forms of the enumerators and context patterns for the text forms. The data structure also includes information extraction rules that are associated with the enumerators. The data structure is updated with additional context patterns and text forms that are identified within a set of documents to which text analytic annotators are to be tuned. The set of documents are analyzed against the updated data structure and additional extraction rules are generated based on the analysis.

Abstract: A data structure is generated containing enumerators for data types of a domain, text forms of the enumerators and context patterns for the text forms. The data structure also includes information extraction rules that are associated with the enumerators. The data structure is updated with additional context patterns and text forms that are identified within a set of documents to which text analytic annotators are to be tuned. The set of documents are analyzed against the updated data structure and additional extraction rules are generated based on the analysis.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for analyzing documents corresponding to demographics. A computer determines whether a first text analysis algorithm corresponds to a demographic of a document, wherein Natural Language Processing (NLP) utilizes text analysis algorithms to produce an analysis of the document and provide annotations. Responsive to determining that the first text analysis algorithm does correspond to the demographic of the document, the computer analyzes the document utilizing the determined corresponding first text analysis algorithm. In another embodiment, the computer determines whether a second text analysis algorithm is available. Responsive to determining that a second text analysis algorithm is not available, the computer provides information from the analysis of the document utilizing one or more text analysis algorithms.

Abstract: Techniques are disclosed for discovering and presenting topic summaries related to a selection of text in an electronic document. A topic summary application receives the user-selected text and identifies entities in the text using natural language processing. Using natural language processing, the summary application also identifies related entities and associated text phrases in a remaining portion of the electronic document. The remaining portion may be a portion of the document that precedes the user-selected text, so that a summary generated therefrom may be used to refresh the memory of the user while not revealing information that the user has not yet encountered. In addition, the summary application determines semantically important text phrases using text analytics and generates a summary, presented to the user in a pop-up window, of most frequently correlated related entities along with text phrases that are semantically important.

Abstract: Techniques are disclosed for discovering and presenting topic summaries related to a selection of text in an electronic document. A topic summary application receives the user-selected text and identifies entities in the text using natural language processing. Using natural language processing, the summary application also identifies related entities and associated text phrases in a remaining portion of the electronic document. The remaining portion may be a portion of the document that precedes the user-selected text, so that a summary generated therefrom may be used to refresh the memory of the user while not revealing information that the user has not yet encountered. In addition, the summary application determines semantically important text phrases using text analytics and generates a summary, presented to the user in a pop-up window, of most frequently correlated related entities along with text phrases that are semantically important.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for analyzing documents corresponding to demographics. A computer determines whether a first text analysis algorithm corresponds to a demographic of a document, wherein Natural Language Processing (NLP) utilizes text analysis algorithms to produce an analysis of the document and provide annotations. Responsive to determining that the first text analysis algorithm does correspond to the demographic of the document, the computer analyzes the document utilizing the determined corresponding first text analysis algorithm. In another embodiment, the computer determines whether a second text analysis algorithm is available. Responsive to determining that a second text analysis algorithm is not available, the computer provides information from the analysis of the document utilizing one or more text analysis algorithms.

Abstract: Provided is a technique for providing an interactive dashboard based on real-time sentiment analysis for synchronous communication. In real time, while monitoring communications between a first user and a second user, a cumulative sentiment score is generated representing a sentiment of the first user during a period of time and an instantaneous sentiment score representing a sentiment of the first user at a current time. In real time, an interactive dashboard is displayed with a visual representation for the first user having a first indicator representing the cumulative sentiment score and a second indicator representing the instantaneous sentiment score.