This disclosure presents a comprehensive system that allows the storage of Meaning in a manner able to be accessed in any supported language, be processed under a rigorous mathematical methodology, and combine the information content of multiple files. Able to search, compare, paraphrase, and excerpt unambiguously by computer programs. Identify and present conflicts between files that an editor may resolve. The system incorporates corruption-resistant technology to prevent malicious damage, and a shell treats language-based documents as assets or NFTs. An integral coin-based asset/currency is designed to be sold, traded, earned, and bartered. A serial number allows that whole coins, stolen or permanently lost, be tracked to a wallet or canceled, reducing theft-reward.

A personnel selecting device includes: a personnel extracting unit which obtains information of a requested service from a client device, extracts personnel matched to the information of the requested service by referring to a job skill storage which stores information indicating a correspondence relationship between a service and personnel, and outputs the extracted personnel information to the client device; a personnel arranging unit which obtains, from the client device, information of personnel selected from among the extracted personnel, and requests the requested service of the selected personnel; and an analyzing unit which obtains, from the client device, information indicating evaluation details for the requested service performed in natural language, determines an evaluation for the evaluation details by analyzing a character string indicated by the evaluation details, and updates the information in the job skill storage based on the determined evaluation.

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.

An artificial intelligence-based semantic recognition method, apparatus, and device. In the artificial intelligence-based semantic recognition method, a pre-trained semantic recognition model is trained by using a training corpus configured by a developer on a model training platform such as a Bot platform and a negative corpus provided on the model training platform, where the negative corpus is extracted by mapping an encoding value of the training corpus to a negative corpus set. Therefore, the negative corpus is extracted based on the encoding value of the training corpus, and a randomized method for generating the negative corpus is changed into a stable method.

The disclosure herein describes providing responses to natural language queries associated with transcripts at least by searching multiple indexes. A transcript associated with a communication among a plurality of speakers is obtained, wherein sets of artifact sections are identified in the transcript. A set of section indexes is generated from the transcript based on artifact type definitions. A natural language query associated with the transcript is analyzed using a natural language model and query metadata of the analyzed natural language query is obtained. At least one section index of the set of section indexes is selected based on the obtained query metadata and that selected at least one section index is searched. A response to the natural language query is provided including result data from the searched at least one search index, wherein the result data includes a reference to an artifact section referenced by the searched section index(es).

There is provided a method and a system for generating an interactive search interface in response to a search request by using at least one machine learning (ML) model. A search request such as one of a word, a sentence, a paragraph, and a document is received, and a semantic representation of the search request is received. Semantically similar documents are received by: comparing the search request semantic representation with document representations to obtain semantic similarity scores, and selecting semantically similar documents based on the scores. For each of the set of semantically similar documents, a respective set of similar paragraphs, sentences, and words are determined based on associated representations. An interactive search interface is generated and displayed to a user interface. A selection of a given document is received, and each of the respective set of similar paragraphs, sentences, and similar words associated with the given document are displayed.

The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

A computer system, computer program product, and a computer-implemented method for supplementing a data governance framework with one or more new data governance technical rules is disclosed. The method comprises providing a plurality of expressions and a first mapping. The expressions assign natural language patterns to technical language patterns. The first mapping maps first terms to data sources. A rule generator receives a new natural language (NL) rule comprising one or more natural-language patterns and one or more first terms. The rule generator resolves the new NL rule into one or more new technical rules interpretable by a respective rule engine and stores the one or more technical rules in a rule repository.

Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog shema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

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.