Visualizations are automatically generated based at least on a received identifier that identifies a report. One or more data sets and one or more style configurations are retrieved from a shared visualization library via a backend API based on the received identifier and passed to a router component. The router component includes a switch that renders a score component, which generates a score visualization, if the retrieved data sets include score data. If the data sets include time series data, or the score visualization also includes a time series graph, the router component renders a time series component that passes the data sets and the style configurations to a parser that parses the data sets and the style configurations and outputs one or more highcharts options objects. A highcharts component generates the time series visualizations and/or adds the time series graph to the score visualization based on the highcharts options objects.

Systems and methods for determining whether an electronic document constitutes vouching evidence is provided. The system may receive ERP item data and generate hypothesis data based thereon, and may receive electronic document data and extract ERP information therefrom. The system may then apply one or more models to compare the hypothesis data to the extracted ERP information to determine whether the electronic document constitutes vouching evidence for the ERP item. Systems and methods for verifying an assertion against a source document are provided. The system may receive first data indicating an unverified assertion and second data comprising a plurality of source documents. The system may apply one or more extraction models to extract a set of key data from the plurality of source documents and may apply one or more matching models to compare the first data to the set of key data to determine whether vouching criteria are met.

A document retrieval system that retrieves documents, with concepts of the documents taken into account, is provided. The document retrieval system (100) includes an input unit (101), a first processing unit (102), a storage unit (105), a second processing unit (103), and an output unit (104). The input unit (101) has a function of inputting a first document (20), the first processing unit (102) has a function of creating a first graph structure (21) from the first document (20), the storage unit (105) has a function of storing a second graph structure (11), the second processing unit (103) has a function of calculating a similarity between the first graph structure (21) and the second graph structure (11), the output unit (104) has a function of supplying information, the first processing unit (102) has a function of dividing the first document (20) into a plurality of tokens, a node and an edge of the first graph structure (21) have a label, and the label includes the plurality of tokens.

A knowledge management system may receive a set of data instances. The system may extract a plurality of entities from the set of data instances. The system may convert the plurality of entities into a plurality of entity embeddings, each entity embedding representing an entity in a latent space. The system may generate a reference embedding that has the same length as the plurality of entity embeddings. The system may compare, for each value in each entity embedding, the value to a corresponding value of the reference embedding. The system may generate a plurality of entity fingerprints, each entity fingerprint corresponding to an entity embedding, each entity fingerprint comprising Boolean values that are generated based on comparing values in each entity embedding to corresponding values of the reference embedding. The system may store the plurality of entity fingerprints to represent the plurality of entities.

A word extraction unit 11 that analyzes m texts to extract n words, a vector computation unit 12 that converts each of the m texts into a q-dimension vector and each of the n words into a q-dimension vector, thereby computing m text vectors including q axis components and n word vectors including q axis components, and an index value computation unit 13 that takes each of inner products of the m text vectors and the n word vectors, thereby computing a similarity index value reflecting a relationship between the m texts and the n words are included, and it is possible to obtain a similarity index value representing which word contributes to which text and to what extent as an inner product value by calculating an inner product of a text vector computed from a text and a word vector computed from a word included in the text.

Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.

A text search method, a text search device, a server and a storage medium are provided, relating to the field of information processing technology. The method acquires a target text matrix formed by a plurality of target word vectors associated with an input text according to a target database by preconfiguring a target database including a plurality of word vectors, a plurality of to-be-matched texts and a subject graph corresponding to each of the to-be-matched texts; then uses that target text matrix to construct a target subject graph corresponding to the input text; acquires in the target database a plurality of the initially matching texts corresponding to the input text and a subject graph corresponding to each of the initially matching texts; then generates a search result of the input text according to the target subject graph and the subject graph corresponding to each of the initially matching texts.

A non-transitory computer readable storage medium with instructions executed by a processor maintains a collection of data access connectors configured to access different sources of unstructured data. A user interface with prompts for designating a selected data access connector from the data access connectors is supplied. Unstructured data is received from the selected data access connector. Numeric vectors characterizing the unstructured data are created from the unstructured data. The numeric vectors are stored and indexed.

In many embodiments, a system comprising one or more processor and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: determining a set of questions associated with a campaign by extracting text from one or more advertisements; generating vector embeddings of one or more online activities of a user; receiving a search query from a graphical user interface of a computing device of the user; determining a second question from the set of questions to present to the user based on respective relevance probabilities; determining a confidence score associated with the second question; and when the confidence score associated with the second question exceeds a second predetermined threshold, presenting the second question to the user via the graphical user interface. Other embodiments of related methods and systems are also provided.

Methods are taught for creating training data for a learning algorithm, training the learning algorithm with the training data and using the trained learning algorithm to suggest domain names to users. A domain name registrar may store activities of a user on a registrar website. Preferably, domain name searches, selected suggested domain names and domain names registered to the user are stored as the training data in a training database. The training data may be stored so that earlier activities act as inputs to the learning algorithm while later activities are the expected outputs of the learning algorithm. Once trained, the learning algorithm may receive activities of other users and suggest domain names to the other users based on their activities.