Abstract: A computer-implemented method applies labels to unlabeled public data for use by a global model. One or more processors train one or more local machine learning models with local private data to create one or more trained models. Processor(s) generate a label for each of the local private data using the one or more trained models, where each label describes the local private data, and then apply the label to unlabeled public data to create labeled public data. One or more processors then input the labeled public data into a global model that uses the public data.

Abstract: A method for determining an object referenced within a set of one or more informal online communications comprises: generating a knowledge graph for a company based at least on formal online communications, the knowledge graph comprising a plurality of node elements, and the knowledge graph further comprising, for each node element of the knowledge graph, a corresponding halo comprising one or more words which are temporally proximate to that node element within the formal online communications; for each node element of the knowledge graph which is determined to be present in a given informal online communication, detecting a halo comprising one or more words which are temporally proximate to that node element within the given informal online communication; and identifying which of the plurality of node elements has a corresponding halo within the knowledge graph most similar to the detected halo, wherein the identified node element is the referenced object.

Abstract: A computer-implemented method applies labels to unlabeled public data for use by a global model. One or more processors train one or more local machine learning models with local private data to create one or more trained models. Processor(s) generate a label for each of the local private data using the one or more trained models, where each label describes the local private data, and then apply the label to unlabeled public data to create labeled public data. One or more processors then input the labeled public data into a global model that uses the public data.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: A method for determining an object referenced within a set of one or more informal online communications comprises: generating a knowledge graph for a company based at least on formal online communications, the knowledge graph comprising a plurality of node elements, and the knowledge graph further comprising, for each node element of the knowledge graph, a corresponding halo comprising one or more words which are temporally proximate to that node element within the formal online communications; for each node element of the knowledge graph which is determined to be present in a given informal online communication, detecting a halo comprising one or more words which are temporally proximate to that node element within the given informal online communication; and identifying which of the plurality of node elements has a corresponding halo within the knowledge graph most similar to the detected halo, wherein the identified node element is the referenced object.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: Embodiments include a system, method, and computer program product for foreign organization name matching. Aspects include receiving a first entity name from a first database configured in a first language and receiving a second database, wherein the second database includes a plurality of entity names in a second language, wherein the first and second languages are different. Aspects also include performing an Internet search based on the first entity name, wherein a language setting of the Internet search is configured to the second language and identifying a list of candidate names based on a set of results from the Internet search. Another aspect includes filtering the list to obtain a resulting candidate name and determining whether the resulting candidate name matches one of the entity names in the second database. Aspects include associating the first entity name and a matching entity name in the second database based on the determination.

Abstract: A language processing application employs a classing function optimized for the underlying production application context for which it is expected to process speech. A combination of class based and word based features generates a classing function optimized for a particular production application, meaning that a language model employing the classing function uses word classes having a high likelihood of accurately predicting word sequences encountered by a language model invoked by the production application. The classing function optimizes word classes by aligning the objective of word classing with the underlying language processing task to be performed by the production application. The classing function is optimized to correspond to usage in the production application context using class-based and word-based features by computing a likelihood of a word in an n-gram and a frequency of a word within a class of the n-gram.

Abstract: A method and apparatus are provided for automatically inducing class based shrinkage features. The method includes clustering each word in a set of word groupings of a given type into a respective one of a plurality of classes. The method further includes selecting and extracting a set of class-based shrinkage features from the set of word groupings based on the plurality of classes. The set of class-based shrinkage features is specifically selected for an intended classification application.