Abstract: Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.

Abstract: In some examples, artificial intelligence based corpus enrichment for knowledge population and query response may include generating, based on annotated training documents, an entity and relation annotation model, identifying, based on application of the entity and relation annotation model to a document set that is to be annotated, entities and relations between the entities for each document of the document set to generate an annotated document set, and categorizing each annotated document into a plurality of categories. Artificial intelligence based corpus enrichment may include determining whether an identified category includes a specified number of annotated documents, and if not, additional annotated documents may be generated for the identified category that may represent a corpus.

Abstract: A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.

Abstract: Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.

Abstract: A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.

Abstract: Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A machine learning document processing system performs natural language processing (NLP) and machine learning to determine a subset of documents from a document dataset based on the structural features and semantic features. The system facilitates an interactive process, e.g., through a client application, to receive user input from a user to identify documents with a specific document feature category. The user input may be provided from a user as speech or text, and NLP is performed on the user input to determine user intent, the document features, and document feature category. Using the user intent and the additional document feature category, the system identifies subsets of the document dataset that matches the document feature category for display.

Abstract: A device may analyze a set of unstructured documents of an organization associated with a domain to identify a first set of entities. The device may analyze a set of semi-structured documents of the organization to determine a second set of entities. The device may filter the first set of entities using the second set of entities. Filtering the first set of entities may include removing, from the first set of entities, one or more entities that do not satisfy a threshold level of similarity with entities included in the second set of entities. The device may consolidate the filtered first set of entities and the second set of entities to identify a set of key entities. The device may provide the set of key entities to a user device to allow the set of key entities to be annotated and used for one or more machine learning models.

Abstract: In some examples, artificial intelligence based corpus enrichment for knowledge population and query response may include generating, based on annotated training documents, an entity and relation annotation model, identifying, based on application of the entity and relation annotation model to a document set that is to be annotated, entities and relations between the entities for each document of the document set to generate an annotated document set, and categorizing each annotated document into a plurality of categories. Artificial intelligence based corpus enrichment may include determining whether an identified category includes a specified number of annotated documents, and if not, additional annotated documents may be generated for the identified category that may represent a corpus.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A computer system for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The computer system may include parsing the extracted table. The computer system may also include performing structural analysis on the parsed extracted table. The computer system may further include selecting at least one structural template based on the structural analysis of the parsed extracted table. Additionally, the computer system may include selecting the at least one narration template based on the at least one selected structural template. The computer system may also include applying the at least one selected narration template to the extracted table. The computer system may further include narrating the extracted table based on the applying of the at least one selected narration template to the extracted table.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may include parsing the extracted table. The method may also include performing structural analysis on the parsed extracted table. The method may further include selecting at least one structural template based on the structural analysis of the parsed extracted table. Additionally, the method may include selecting the at least one narration template based on the at least one selected structural template. The method may also include applying the at least one selected narration template to the extracted table. The method may further include narrating the extracted table based on the applying of the at least one selected narration template to the extracted table.

Abstract: According to one exemplary embodiment, a method for finding an answer to a query from a table is provided. The method may include receiving the query and the table. The method may also include generating a hierarchical representation based on the received table, whereby the hierarchical representation comprises a primary tree and a secondary tree. The method may then include mapping the primary tree to the secondary tree. The method may further include generating a plurality of predicate triples in a semantic relationship form based on the primary tree and the secondary tree. The method may also include determining if a predicate triple within the plurality of predicate triples matches the query using query-side and table-side context and semantics. The method may then include adding the predicate triple within the plurality of predicate triples to a plurality of candidate answers based on determining that the predicate triple matches the query.

Abstract: A device may analyze a set of unstructured documents of an organization associated with a domain to identify a first set of entities. The device may analyze a set of semi-structured documents of the organization to determine a second set of entities. The device may filter the first set of entities using the second set of entities. Filtering the first set of entities may include removing, from the first set of entities, one or more entities that do not satisfy a threshold level of similarity with entities included in the second set of entities. The device may consolidate the filtered first set of entities and the second set of entities to identify a set of key entities. The device may provide the set of key entities to a user device to allow the set of key entities to be annotated and used for one or more machine learning models.

Abstract: A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.

Abstract: A computer-implemented method includes receiving a question set. The question set is for a question answer system. The method receives a plurality of domain cluster topics. The plurality of domain cluster topics are for the question set. The method organizes the question set based on the plurality of domain cluster topics to yield a plurality of question set topics. The method receives a document corpus. The document corpus is for the question answer system. The method organizes the document corpus based on the plurality of domain cluster topics to yield a plurality of document corpus topics. The method maps the plurality of question set topics to the plurality of document corpus topics. The method determines whether any of the plurality of question set topics are unmatched. The method is responsive to any of the plurality of question set topics being unmatched. The method requests enrichment for the document corpus.

Abstract: A machine learning document processing system performs natural language processing (NLP) and machine learning to determine a subset of documents from a document dataset based on the structural features and semantic features. The system facilitates an interactive process, e.g., through a client application, to receive user input from a user to identify documents with a specific document feature category. The user input may be provided from a user as speech or text, and NLP is performed on the user input to determine user intent, the document features, and document feature category. Using the user intent and the additional document feature category, the system identifies subsets of the document dataset that matches the document feature category for display.