Abstract: A method is provided for efficiently providing sentiments or other manual labels for textual training data. The method includes using an embedding model to project acquired user text to an embedding vector in an embedding space. Distances (e.g., cosine similarities) between this embedding vector and the embedding vectors determined for a plurality of already-label user text training examples are then determined. The already-labeled user text that has the shortest distance is determined and the label thereof is prospectively applied to the acquired user text and presented to a user for approval. The user can approve the prospectively applied label, in which case the newly acquired text is added to the training data with the prospectively applied label associated therewith for later use in training a language model. Alternatively, the user can decline the prospectively applied label and apply an alternative label to the newly acquired text.

Abstract: Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

Abstract: Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

Abstract: Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

Abstract: A chatbot system is disclosed that enables users to apply for opportunities (e.g., employment opportunities, educational opportunities, lending opportunities) via a chat interface. For example, a chatbot hosted by the chatbot system can receive a natural language request from a user and determine that the intent of the user message is to submit an application related to opportunity. In response, the chatbot may collect applicant information from various sources (e.g., user documents, social media accounts, user messages) to apply for the opportunity on behalf of the user. The disclosed chatbot system enhances user experience by enabling the application process to proceed via natural language messages and prompts, reducing the amount of applicant information that is provided directly by the user, reducing the number of mistakes in the application process, and maximizing the benefit to the user by enabling the application to multiple or different opportunities with minimal additional effort.

Abstract: Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

Abstract: Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

Abstract: Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.

Abstract: Representative embodiments disclose mechanisms to validate statements made as part of a document creation or editing process. As the user edits or creates a document using a document creation/editing application, statements made in the document can be submitted to a validation service in a proactive or reactive manner. The statement validation service receives the statement, resolves any coreferences using a coreference resolution process. Once coreferences in the statement have been resolved and replaced, entities are resolved using an entity resolution process. Predicates are then resolved using a predicate resolution process. Entity-predicate pairs are then used to traverse a knowledge graph to extract information relevant to the statement. Suggested corrections are created from the extracted information and presented to the user via the document creation/editing application.

Abstract: Representative embodiments disclose mechanisms to validate statements made as part of a document creation or editing process. As the user edits or creates a document using a document creation/editing application, statements made in the document can be submitted to a validation service in a proactive or reactive manner. The statement validation service receives the statement, resolves any coreferences using a coreference resolution process. Once coreferences in the statement have been resolved and replaced, entities are resolved using an entity resolution process. Predicates are then resolved using a predicate resolution process. Entity-predicate pairs are then used to traverse a knowledge graph to extract information relevant to the statement. Suggested corrections are created from the extracted information and presented to the user via the document creation/editing application.

Abstract: Example natural language processing systems and methods are described. In one implementation, a system receives a request from a remote system, where the request includes text data or voice data. The system analyzes the text data or voice data to determine an intent associated with the request. Based on the intent associated with the request, the system generates a response to the request and communicates the response to the remote system.