4 results about "Feature term" patented technology

The present application relates to the field of computer technology application, and specifically provides a kind of man-machine interaction method, device, electronic equipment and storage medium, comprising: based on the current received inquiry input information, obtain k associated labels from the preset feature term knowledge base;Current received inquiry input information and corresponding k associated labels are mapped using a large language model, to obtain corresponding mapping label;Mapping label is added to the current combination label set, and based on the current combination label set and the preset knowledge graph, the symptoms associated with the current combination label set are obtained as the current inquiry label;If the preset termination condition is met, the corresponding inquiry output information is output, otherwise, the next inquiry question is generated;Based on the received answer information as the current received inquiry input information, the next inquiry operation is carried out.The present application can improve the man-machine interaction precision.

This specification discloses methods, apparatus, devices, and systems for determining a feature effective value of business data. In one implementation, a method includes: obtaining a joint data share of a first participant based on joint data that includes feature values of a plurality of objects corresponding to a plurality of feature terms, obtaining a predictive value share and a model parameter share based on the joint data and a business prediction model, determining, through secure multi-party computation, a correlation data share corresponding to the plurality of participants, and determining, through a significance test method, an effective value of a feature term of the plurality of feature terms.

The application belongs to the technical field of artificial intelligence language information processing, and particularly relates to a service specific field scarce resource language model modeling method, which comprises the following steps: step 1) obtaining a preprocessed sentence set composed of multiple segmented sentences; step 2) obtaining a sentence-topic distribution and a topic-word distribution; step 3) obtaining selected field characteristic words; step 4) obtaining translated field characteristic words; step 5) calculating and obtaining a field topic distribution by using the translated field characteristic words; step 6) calculating the correlation between the sentence-topic distribution and the field topic distribution of each segmented sentence; step 7) judging whether the correlation of each segmented sentence exceeds a preset threshold value, and selecting the sentences meeting the condition into the language model training corpus of the scarce resource specific field; step 8) obtaining the final language model training corpus; and step 9) training the scarce resource language model to obtain the scarce resource language model.

The application provides an information identification method and device, computer equipment, a storage medium and a program product, and relates to the technical fields of artificial intelligence, big data, intelligent transportation and the like. A text feature vector of a to-be-identified text is obtained. Whether the to-be-identified text is spam is determined based on the similarity between the sample feature vectors of each sample in a sample library and the text feature vector. For each spam category, a positive context feature library corresponding to the spam category is obtained based on a positive sample set of the spam category. A negative context feature library corresponding to the spam category is obtained based on a negative sample set of the spam category. Feature words of each spam category are obtained. The positive and negative context feature libraries, topic words, category keywords or entity name words of each spam category are used to obtain the sample feature vectors of each sample in the sample library, so that the text can be identified based on multi-dimensional features, and the accuracy of identification is improved.