In an example embodiment, input is received from a first user of a computer system. A text object relating to a first item from the input is created, and translated from a first language to a second language. A plurality of text objects, in the second language, having text similar to the translated text object, are located in a database, each text object comprising textual information pertaining to the first item. The plurality of text objects having text similar to the translated text are then ranked based on a comparison of the contextual information about the first item and the contextual information stored in the database for the plurality of text objects having text similar to the translated text object. At least one of the ranked text objects is translated to the first language.

A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.

Textual information related to user information from user service information is identified. A layered matching is performed on the textual information based on preset background identification information in a preset list, wherein the layered matching includes different matching methods, and the preset list includes a plurality of entries storing different preset background identification information related to the user information. The user information is determined based on the layered matching.

Provided is a natural language query answering method. The natural language query answering method includes generating a query axiom from an input query, generating answer candidates from the input query, filtering the answer candidates based on a similarity between the query axiom and the answer candidates, reasoning out the answer candidates by using at least one of an inductive reasoning method, a deductive reasoning method, and an abductive reasoning method, calculating reliability of the answer candidates, determining ranks of the answer candidates based on the calculated reliability, and comparing a threshold value with a reliability ratio of reliability of an answer candidate determined as No. 1 rank to reliability of an answer candidate determined as No. 2 rank, readjusting the determined ranks according to a result of the comparison, and detecting a No. 1 rank answer candidate, determined through the readjustment, as a final answer.

A computerized system and methods are provided for the automated extraction of contextually relevant information, and the automatic processing of actionable information from generic document sets. More specifically, automated systems and techniques for the extraction and processing of opportunity documents, are provided, which avoid inaccuracies and inefficiencies resulting from conventional and/or human-based document processing techniques.

This document describes techniques and devices for a refined search with machine learning. These techniques improve computer-aided searches through enabling selection of search criteria used in a prior search and providing a refined search result based on that selection. Furthermore, a machine-learning component of a search engine can be altered to improve future search results based on the selection and an indication of the desirability of the refined search result.

A computer extracts from contact records that each include a contact identifier, a group identifier for each group with which the contact has had an interaction, and interaction information that indicates a number of interactions and a timing of a most recent interaction. The contact data records are processed to generate a contact profile record for each contact including group metric values and a corresponding value for each group metric value based on an interaction history of groups the contact has interacted with. An interaction analytics databases stores a set of contact profile records and group profile records for groups that include metric values associated with the group and an interaction history. They are processed with at least thousands of the contact profile records to determine group-contact compatibility factors. A compatibility parameter is generated and communicated for each of at least thousands of contacts based on the group-contact compatibility parameters.

A communication server, interacting with an organization system having users that wish to communicate securely, provides secure communication capability to the users, without the communication server itself having access to unencrypted content of the user communications or to cryptographic keys that would allow the communication server to derive the unencrypted content. Thus, the communication server that provides the secure communication capability need not itself be trusted by the users with access to communicated content. To achieve this, the various entities communicate to exchange cryptographic keys in such a manner that the communication server never obtains usable copies of the cryptographic keys. Secure search capability is also provided by the client devices supplying a set of message tokens obtained by transformations that the communication server cannot replicate, and the communication server maintaining a search index storing the message tokens in association with the (encrypted) messages from which they were obtained.

A method for outputting a search result includes: executing a reception process that includes receiving a search query for target data; executing a candidate item identification process that includes referring to index information associating each of a plurality of items included in the target data with a position of a corresponding one of the items, and identifying a first storage area configured to store an item corresponding to a keyword included in the search query; and executing an addition process that includes when a description included in the corresponding one of the items includes a reference to a different item, referring to the index information, and adding information on a second storage area configured to store the different item to the reference to the different item.

Method, apparatus and computer program product for querying a group-based communication platform are described herein. For example, the apparatus comprises at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least store a plurality of electronic messages to a message corpus; receive a search query associated with the message corpus, wherein the search query comprises reaction metadata criteria; and apply the search query to the message corpus to generate a reaction search results set.