Abstract: A document is divided into sections based on a characteristic of the text in the document. Characteristics may include specific characters such as paragraph breaks or selected punctuation, the topics or categories of the text, or a quantity of text in each section. Each section of the document may be represented by an embedding vector in a semantic embedding space. Values are determined based on the text in each section and the semantic characteristics of each section, such as word order, capitalization, punctuation, and word meaning. When a query is received, a vector value representing the query is determined based on the text and semantic characteristics of the query. Based on the similarity between the values determined for the query and those determined for the sections of a document, the specific section of a potentially large document that most closely matches the query is determined and included in a response.

Abstract: A system creates and searches knowledge base (KB) articles inside an organization while supporting operation as a SaaS (Software as-a-Service) across a plurality of organizations. The system implements a real-time online learning-to-rank (L2R) algorithm for learning relevance scoring that is customized to each organization. This algorithm incorporates rich lexical features using a query similarity kernel. A scoring function includes a pairwise static module, which may be trained off-line using training data, and a lexical adaptive module, which is trained on-line based on user feedback. The scoring function makes the system easy to deploy, modify, and suitable for handling events that naturally happen over the lifecycle of any KB deployment, without manual training.

Abstract: A system stores resources such as text articles, videos, and so forth for an organization. During operation, the system receives a query and provides a response. During initial use of the system, there is little or no historical data available to help determine which resource is most relevant to a particular query. In this “cold-start” situation, the system determines attributes associated with a user account of the user making the query. The query is used to search a data store and retrieve a set of resources based on a term match with the query and to find the resources which correspond to the attributes of the user account. This allows the system to provide simplified output that is more likely to be relevant to that particular user in the “cold-start” situation.

Abstract: A system stores resources such as text articles, videos, and so forth for an organization. During operation, the system receives a query and provides a response. During initial use of the system, there is little or no historical data available to help determine which resource is most relevant to a particular query. In this “cold-start” situation, the system determines attributes associated with a user account of the user making the query. The query is used to search a data store and retrieve a set of resources based on a term match with the query and to find the resources which correspond to the attributes of the user account. This allows the system to provide simplified output that is more likely to be relevant to that particular user in the “cold-start” situation.