Abstract: A method includes receiving, by a question answering system having a confidence threshold, plural questions from one or more user devices. The method includes processing each one of the questions by: generating an answer to the one of the questions; determining a confidence score of the answer; in response to determining the confidence score is greater than the confidence threshold, increasing the confidence threshold and returning the answer to the user device that generated the one of the questions; and in response to determining the confidence score is less than the confidence threshold, decreasing the confidence threshold and not returning the answer to the user device that generated the one of the questions. The increasing the confidence threshold and the decreasing the confidence threshold are performed such that the question answering system returns answers for the plural questions at a frequency that approximates a pre-defined target answering frequency.

Abstract: A method includes receiving, by a question answering system having a confidence threshold, plural questions from one or more user devices. The method includes processing each one of the questions by: generating an answer to the one of the questions; determining a confidence score of the answer; in response to determining the confidence score is greater than the confidence threshold, increasing the confidence threshold and returning the answer to the user device that generated the one of the questions; and in response to determining the confidence score is less than the confidence threshold, decreasing the confidence threshold and not returning the answer to the user device that generated the one of the questions. The increasing the confidence threshold and the decreasing the confidence threshold are performed such that the question answering system returns answers for the plural questions at a frequency that approximates a pre-defined target answering frequency.

Abstract: A mechanism is provided to implement a summarization mechanism for summarizing an identified natural language document using hyperbolic embeddings. Responsive to receiving a query from a user for a summarization of the identified natural language document, the summarization mechanism produces a hyperbolic embedding model of embeddings of the query. The summarization mechanism compares the embeddings of the query to each of a set of embeddings associated with a set of sentences of the identified natural language document. Responsive to identifying a subset of embeddings associated with the set of sentences of the identified natural language document having a semantic specificity to a subset of embeddings associated with the query, the summarization mechanism adds the sentence to a summary of the identified natural language document. The summarization mechanism then outputs the summary to the user.

Abstract: A method overfits a word vector generating process to identify implicit relationships between two or more terms in a corpus. A server identifies instances of multiple user-generated pairs of terms in an original corpus of documents, in which the terms are labeled but a relationship between two or more of the corpus terms are not identified. The server then extracts sentences, from the original corpus of documents, that contain one or more of the multiple user-generated pairs of terms, and combines the sentences into a training corpus, which is used to purposely overfit a word embedding model. This word embedding model leads to a vector that is used to identify other terms that have a same type of relationship as that found in the multiple user-generated pairs of terms, such that search corpus of documents can be searched for similar terms that trained the word embedding model.

Abstract: A mechanism is provided to implement a summarization mechanism for summarizing an identified natural language document using hyperbolic embeddings. Responsive to receiving a query from a user for a summarization of the identified natural language document, the summarization mechanism produces a hyperbolic embedding model of embeddings of the query. The summarization mechanism compares the embeddings of the query to each of a set of embeddings associated with a set of sentences of the identified natural language document. Responsive to identifying a subset of embeddings associated with the set of sentences of the identified natural language document having a semantic specificity to a subset of embeddings associated with the query, the summarization mechanism adds the sentence to a summary of the identified natural language document. The summarization mechanism then outputs the summary to the user.

Abstract: A method overfits a word vector generating process to identify implicit relationships between two or more terms in a corpus. A server identifies instances of multiple user-generated pairs of terms in an original corpus of documents, in which the terms are labeled but a relationship between two or more of the corpus terms are not identified. The server then extracts sentences, from the original corpus of documents, that contain one or more of the multiple user-generated pairs of terms, and combines the sentences into a training corpus, which is used to purposely overfit a word embedding model. This word embedding model leads to a vector that is used to identify other terms that have a same type of relationship as that found in the multiple user-generated pairs of terms, such that search corpus of documents can be searched for similar terms that trained the word embedding model.