Systems and methods for performing machine learning on multilingual text data.

A method, system and computer program product are provided. For each keyword that is visible on a display device, scanning the content that is stored on a user device, or is accessed from a network connection to identify and extract keywords. Further provided is cross-referencing the extracted keywords with a corpus of scored keywords. Based on the extracted keywords being found in the corpus of scored keywords, expanding and/or abridging any of the extracted keywords based on a score in the corpus of scored keywords prior to displaying the modified content on the display device. wherein the extracted keywords match a keyword in the corpus.

A method, system and computer program product are provided. For each keyword that is visible on a display device, scanning the content that is stored on a user device, or is accessed from a network connection to identify and extract keywords. Further provided is cross-referencing the extracted keywords with a corpus of scored keywords. Based on the extracted keywords being found in the corpus of scored keywords, expanding and/or abridging any of the extracted keywords based on a score in the corpus of scored keywords prior to displaying the modified content on the display device. wherein the extracted keywords match a keyword in the corpus.

A method, system and computer program product are provided. For each keyword that is visible on a display device, scanning the content that is stored on a user device, or is accessed from a network connection to identify and extract keywords. Further provided is cross-referencing the extracted keywords with a corpus of scored keywords. Based on the extracted keywords being found in the corpus of scored keywords, expanding and/or abridging any of the extracted keywords based on a score in the corpus of scored keywords prior to displaying the modified content on the display device. wherein the extracted keywords match a keyword in the corpus.

A method, system and computer program product are provided. For each keyword that is visible on a display device, scanning the content that is stored on a user device, or is accessed from a network connection to identify and extract keywords. Further provided is cross-referencing the extracted keywords with a corpus of scored keywords. Based on the extracted keywords being found in the corpus of scored keywords, expanding and/or abridging any of the extracted keywords based on a score in the corpus of scored keywords prior to displaying the modified content on the display device. wherein the extracted keywords match a keyword in the corpus.

A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

Examples of a textual entailment generation system are provided. The system obtains a query from a user and implements an artificial intelligence component to identify a premise, a word index, and a premise index associated with the query. The system may implement a first cognitive learning operation to determine a plurality of hypothesis and a hypothesis index corresponding to the premise. The system may generate a confidence index for each of the plurality of hypothesis based on a comparison of the hypothesis index with the premise index. The system may determine an entailment value, a contradiction value, and a neutral entailment value based on the confidence index for each of the plurality of hypothesis. The system may generate an entailment result relevant for resolving the query comprising the plurality of hypothesis along with the corresponding entailed output index.

Examples of a textual entailment generation system are provided. The system obtains a query from a user and implements an artificial intelligence component to identify a premise, a word index, and a premise index associated with the query. The system may implement a first cognitive learning operation to determine a plurality of hypothesis and a hypothesis index corresponding to the premise. The system may generate a confidence index for each of the plurality of hypothesis based on a comparison of the hypothesis index with the premise index. The system may determine an entailment value, a contradiction value, and a neutral entailment value based on the confidence index for each of the plurality of hypothesis. The system may generate an entailment result relevant for resolving the query comprising the plurality of hypothesis along with the corresponding entailed output index.

The present disclosure involves systems, software, and computer implemented methods for creating line item information from tabular data. One example method includes receiving event data values at a system. Column headers of columns in the event data values are identified. At least one column header is not included in standard line item terms used by the system. Column values of the columns in the event data values are identified. The identified column headers and the identified column values are processed using one or more models to map each column to a standard line item term used by the system. The processing includes using context determination and content recognition to identify standard line item terms. An event is created in the system, including the creation of line items from the identified column value. Each line item includes standard line item terms mapped to the columns.