Abstract: A computer-implemented method, system and computer program product for sharing and utilizing healthcare data, by: providing one or more computer-implemented machine learning models for analyzing the healthcare data; and recording transactions involving the machine learning models using a blockchain as a distributed ledger that is shared, replicated and synchronized. Healthcare data is also used to train the machine learning models. The healthcare data comprises research data or patient data such as Electronic Medical Records (EMRs). A smart contract that is a computer-implemented protocol is used to facilitate, verify or enforce negotiation of the transactions involving the machine learning models or healthcare data.

Abstract: A mechanism is provided for implementing an anticipatory analytics processing mechanism for processing electronic medical records (EMRs) of a set of patients using anticipatory analytics to provide patient insights. Responsive to receiving a set of EMRs for a set of patients, each EMR in the set of EMRs is queued into one of a plurality of queues based on a set of work types. Responsive to receiving a request for work by a processing service indicating a work type to be provided, an EMR is identified from the plurality of queues based on the work type to be provided and a predetermined order of work. The identified EMR is dispatched to the processing service. Responsive to receiving a completion indication from the processing service, the EMR is either advanced to a queue associated with a next work type category or indicated that patient insight for a patient associated with the EMR is complete.

Abstract: A method of managing an electronic medical record comprises receiving patient data associated with the medical record. First encounter information in the patient data is identified and compared with second encounter information in a first encounter bundle in the medical record. Based on the comparing, the first encounter information and second encounter information are determined to be associated with the same encounter. The patient data is added to the first encounter bundle based on the determining. The first encounter bundle is analyzed, resulting in a computer-readable artifact for the first encounter bundle. A summary of the first encounter bundle is updated based in part on the computer-readable artifact.

Abstract: A computer-implemented method, system and computer program product for sharing and utilizing healthcare data, by: providing one or more computer-implemented machine learning models for analyzing the healthcare data; and recording transactions involving the machine learning models using a blockchain as a distributed ledger that is shared, replicated and synchronized. Healthcare data is also used to train the machine learning models. The healthcare data comprises research data or patient data such as Electronic Medical Records (EMRs). A smart contract that is a computer-implemented protocol is used to facilitate, verify or enforce negotiation of the transactions involving the machine learning models or healthcare data.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate scheduling processing and summarization of an electronic medical record based on a summarization deadline are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a task manager component that can determine a processing priority of one or more data bundles of an electronic medical record based on a summarization deadline of the electronic medical record. The computer executable components can further comprise a cognitive analysis component that can employ an artificial intelligence model to process the one or more data bundles based on the processing priority.

Abstract: A computer-implemented method can include identifying a first set of text samples that include a particular potentially offensive term. Labels can be obtained for the first set of text samples that indicate whether the particular potentially offensive term is used in an offensive manner. A classifier can be trained based at least on the first set of text samples and the labels, the classifier being configured to use one or more signals associated with a text sample to generate a label that indicates whether a potentially offensive term in the text sample is used in an offensive manner in the text sample. The method can further include providing, to the classifier, a first text sample that includes the particular potentially offensive term, and in response, obtaining, from the classifier, a label that indicates whether the particular potentially offensive term is used in an offensive manner in the first text sample.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for building language models. One of the methods includes identifying a first group of one or more users associated with a user in a social network. The method includes identifying first linguistic information associated with the first group. The method includes generating a first language model based on the first linguistic information. The method includes identifying a second group of one or more users associated with the user. The method includes identifying second linguistic information associated with the second group. The method includes generating a second language model based on the second linguistic information. The method includes associating the first language model and the second language model with the user.

Abstract: This document describes, among other things, a computer-implemented method. The method can include obtaining a plurality of text samples that each include one or more terms belonging to a first class of terms. The plurality of text samples can be classified into a plurality of groups of text samples. Each group of text samples can correspond to a different sub-class of terms. For each of the groups of text samples, a sub-class context model can be generated based on the text samples in the respective group of text samples. Particular ones of the sub-class context models that are determined to be similar can be merged to generate a hierarchical set of context models. Further, the method can include selecting particular ones of the context models and generating a class-based language model based on the selected context models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for acoustic model generation. One of the methods includes identifying one or more demographic characteristics for a user of a social networking site. The method includes receiving speech data from the user, the speech data associated with a user device. The method includes storing the speech data associated with demographic characteristics of the user and the user device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating language models. In some implementations, data is accessed that indicates a set of classes corresponding to a concept. A first language model is generated in which a first class represents the concept. A second language model is generated in which second classes represent the concept. Output of the first language model and the second language model is obtained, and the outputs are evaluated. A class from the set of classes is selected based on evaluating the output of the first language model and the output of the second language model. In some implementations, the first class and the second class are selected from a parse tree or other data that indicates relationships among the classes in the set of classes.

Abstract: A system for processing analytics on a cluster of computing resources may receive a user request to process a Job, Service or Reservation, and may include an Orchestrator, Resource Manager, Process Manager, and one or more Agents and Job Drivers, which together deploy the Job onto one or more nodes in the cluster for parallelized processing of Jobs and their associated work items.

Abstract: Methods and systems for facilitating development of voice-enabled applications are provided. The method may comprise receiving, at a computing device, a plurality of actions associated with a given application, parameters associated with each respective action, and example instructions responsive to respective actions. The method may also comprise determining candidate instructions based on the actions, parameters, and example instructions. Each candidate instruction may comprise one or more grammars recognizable by a voice interface for the given application. The method may further comprise the computing device receiving respective acceptance information for each candidate instruction, and comparing at least a portion of the respective acceptance information with a stored acceptance information log comprising predetermined acceptance information so as to determine a correlation.

Abstract: Some implementations include a computer-implemented method. The method can include providing a training set of text samples to a semantic parser that associates text samples with actions. The method can include obtaining, for each of one or more of the text samples of the training set, data that indicates one or more domains that the semantic parser has associated with the text sample. For each of one or more domains, a subset of the text samples of the training set can be generated that the semantic parser has associated with the domain. Using the subset of text samples associated with the domain, a language model can be generated for one or more of the domain. Speech recognition can be performed on an utterance using the one or more language models that are generated for the one or more of the domains.

Abstract: This document describes, among other things, a computer-implemented method. The method can include obtaining a plurality of text samples that each include one or more terms belonging to a first class of terms. The plurality of text samples can be classified into a plurality of groups of text samples. Each group of text samples can correspond to a different sub-class of terms. For each of the groups of text samples, a sub-class context model can be generated based on the text samples in the respective group of text samples. Particular ones of the sub-class context models that are determined to be similar can be merged to generate a hierarchical set of context models. Further, the method can include selecting particular ones of the context models and generating a class-based language model based on the selected context models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating language models. In some implementations, data is accessed that indicates a set of classes corresponding to a concept. A first language model is generated in which a first class represents the concept. A second language model is generated in which second classes represent the concept. Output of the first language model and the second language model is obtained, and the outputs are evaluated. A class from the set of classes is selected based on evaluating the output of the first language model and the output of the second language model. In some implementations, the first class and the second class are selected from a parse tree or other data that indicates relationships among the classes in the set of classes.

Abstract: A computer-implemented method can include identifying a first set of text samples that include a particular potentially offensive term. Labels can be obtained for the first set of text samples that indicate whether the particular potentially offensive term is used in an offensive manner. A classifier can be trained based at least on the first set of text samples and the labels, the classifier being configured to use one or more signals associated with a text sample to generate a label that indicates whether a potentially offensive term in the text sample is used in an offensive manner in the text sample. The method can further include providing, to the classifier, a first text sample that includes the particular potentially offensive term, and in response, obtaining, from the classifier, a label that indicates whether the particular potentially offensive term is used in an offensive manner in the first text sample.

Abstract: Systems, apparatus and method for speech and semantic parsing for content selection. In an aspect, a method includes selecting, for each of a plurality of voice query analyzers, an analyzer output parameter; generating a voice query model for voice queries, the voice query model including analysis fields, wherein each analysis field in at least a first portion of the analysis fields corresponds to a corresponding analyzer output parameter; receiving, from a plurality of content item providers, voice query selection data that describes analyzer output parameter values for the voice query model that satisfy selection criteria for the content item provider; and persisting the voice query selection data for the content item providers to a computer memory device; wherein the voice query analyzers include a semantic analyzer and a biometric analyzer.

Abstract: Some implementations include a computer-implemented method. The method can include providing a training set of text samples to a semantic parser that associates text samples with actions. The method can include obtaining, for each of one or more of the text samples of the training set, data that indicates one or more domains that the semantic parser has associated with the text sample. For each of one or more domains, a subset of the text samples of the training set can be generated that the semantic parser has associated with the domain. Using the subset of text samples associated with the domain, a language model can be generated for one or more of the domain. Speech recognition can be performed on an utterance using the one or more language models that are generated for the one or more of the domains.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training recognition canonical representations corresponding to named-entity phrases in a second natural language based on translating a set of allowable expressions with canonical representations from a first natural language, which may be generated by expanding a context-free grammar for the allowable expressions for the first natural language.

Abstract: A method includes accessing data specifying a set of actions, each action defining a user device operation and for each action: accessing a corresponding set of command sentences for the action, determining first n-grams in the set of command sentences that are semantically relevant for the action, determining second n-grams in the set of command sentences that are semantically irrelevant for the action, generating a training set of command sentences from the corresponding set of command sentences, the generating the training set of command sentences including removing each second n-gram from each sentence in the corresponding set of command sentences for the action, and generating a command model from the training set of command sentences configured to generate an action score for the action for an input sentence based on: first n-grams for the action, and second n-grams for the action that are also second n-grams for all other actions.