319 results about "Search words" patented technology

The invention discloses a medical dialogue search term rewriting method and device, equipment and a storage medium. The method comprises the following steps: detecting whether current dialogue input is a question or has intention jump through a lightweight model for multi-round dialogue intention jump and question recognition; when it is detected that the current dialogue input is subjected to intention hopping or not questioning, the historical dialogue context is emptied, and the current input serves as independent query to be directly output; and when it is detected that the current dialogue input does not have intention hopping and the current dialogue input is a question, triggering the functions of substitution disambiguation and semantic completion, calling the fine-tuning and rewriting large model, and generating a standard medical question with complete semantics and standard terms, so that the context dependency relationship in the user dialogue can be accurately identified, and the user experience is improved. In addition, a large model can be called to complete deep semantic reconstruction when necessary, light model pre-judgment and large model accurate rewriting are combined, accuracy and efficiency are both considered, and the method is a key technical breakthrough for improving usability and specialty of a medical RAG system.

Disclosed in the present invention is a knowledge graph-based unified fault search method for a specialized vehicle device. The method comprises: acquiring device maintenance related data, and taking a device as a core to establish association relationships between the device maintenance related data; acquiring a fault report, and parsing the fault report to obtain parsing result data; constructing a device fault graph on the basis of the device maintenance related data and the parsing result data; integrating the device fault graph into a knowledge graph platform; receiving a search term entered by a current user in a search box, acquiring search results corresponding to the search term, and displaying the search results on a search result page, wherein the search results comprise a result list, data classification statistics, search result sorting, a knowledge cloud picture, and associated recommendations; and providing in the search result page an operation entry for knowledge exploration, and viewing related knowledge exploration of the current knowledge by the user by triggering the operation entry for knowledge exploration. The method solves the problem in the prior art that the search results are not accurate enough.

The invention belongs to the technical field of natural language processing and knowledge engineering, and discloses a method, a system, equipment and a medium for automatically constructing a multi-field scientific knowledge base, and the method comprises the following steps: based on an input identifier list or a field search word, retrieving a full text of a literature, analyzing and converting the full text into a structured text; combining a pre-configured large language model with a field cue word, extracting key information in the structured text, and outputting structured data; the automatic script filters irrelevant, repeated or incomplete structured data according to a pre-configured filtering rule; performing standardization processing on the filtered structured data so as to realize the consistency and comparability of the data; and inserting the standardized structured data into an interactive knowledge base or database, establishing data association, and verifying association logic. The method supports cross-field rapid adaptation, solves the problems of low efficiency and high error rate of a traditional method, and can be widely applied to the fields of biomedicine, material science, synthetic biology and the like.

The invention discloses a position searching method and device based on implicit intention recognition, and relates to the technical field of information retrieval and intelligent recommendation. The method comprises the following steps: sampling from a historical log and constructing prompt words, and performing weighted voting labeling on keywords and resume pairs by utilizing a plurality of large language models to generate an intention recognition training sample; and the cue word is iteratively optimized through manual evaluation, so that the labeling accuracy is ensured. And training an intention recognition model by using the samples. In the online search stage, structural features of search terms and resumes of a user are extracted and input into the pre-training model to recognize the implicit job hunting intention of the pre-training model. Finally, personalized retrieval conditions are dynamically generated according to the intention recognition result, and related positions are recalled and sorted from the position database. According to the method, the accuracy and the individuation degree of position searching are effectively improved, and the user experience of job seekers is remarkably improved.

The invention discloses a search intention recognition method and device, equipment and a storage medium, and relates to the technical field of reinforcement learning, and the method comprises the following steps: generating training data based on an original corpus; performing supervision fine tuning processing on the lightweight language model based on the training data to obtain a fine tuning trained lightweight language model; performing strategy optimization on the lightweight language model subjected to fine tuning training through an improved GRPO reinforcement learning algorithm to obtain a lightweight language model subjected to reinforcement training; constructing a double-model hybrid architecture based on a short thinking chain model and a thinking chain-free model in the lightweight language model subjected to enhanced training; and outputting a final sequence of the related documents of the search terms of the user through the double-model hybrid architecture. According to the technical scheme of lightweight language model fine tuning, reinforcement learning optimization and double-model hybrid reasoning, the target search scene recognition accuracy is improved, the real-time response speed is high, and training time consumption and reasoning resource requirements are reduced.

An online system provides a search interface for a user to identify items. The search interface may present suggested search queries to the user, allowing the user to select a suggested search query rather than manually entering search terms to form a search query. To identify search queries most likely to be selected by the user, the online system gets a set of candidate search queries and generates a relevance score for each candidate search query by applying a trained query relevance model to each candidate search query. The scored candidate search queries are selected and ranked using the relevance scores, and the selected candidate search queries are displayed using the ranking in the search interface. The query relevance model is a transformer-based small language model receiving a user sequence of prior search queries and items with which the user interacted and the candidate search terms as input.

The invention provides an article recommendation method and device, and the method comprises the steps: determining a target word from a search word set; obtaining external platform domain data corresponding to the target word, matching the external platform domain data with the internal platform domain data, and determining the matching similarity between the external platform domain data and the internal platform domain data; and taking the internal platform domain data and the external platform domain data of which the matching similarity exceeds a first threshold value as target internal platform domain data and target external platform domain data, and constructing product word data based on the target internal platform domain data and the target external platform domain data so as to perform article recommendation based on the product word data. According to the embodiment, the hotspot trend can be responded in time, and the cold start period of the article related to the hotspot trend is shortened, so that the timeliness and accuracy of article recommendation are improved, and the user experience is improved.

The invention discloses an advertisement design display system and method based on big data. The system comprises a data acquisition layer for acquiring multi-source data sources; the semantic understanding layer is used for generating semantic vectors of search words and advertising words based on a BERT-Chine pre-training language model, calculating a quantitative semantic matching degree through cosine similarity, performing deep semantic understanding and synonym equivalence matching, performing intention classification on features through an XGBoost gradient promotion decision tree, and judging that a user is in an instant purchase, decision period or interest exploration state; the scene sensing layer is used for dynamically recognizing a scene where a user is located by using an LSTM-CRF time sequence model; the feature recombination layer is used for extracting explicit features and mining implicit correlation features; the deep matching layer is used for generating a comprehensive matching score of a 0-1 interval by fusing explicit features and implicit association features in combination with multi-task learning, and performing screening according to the comprehensive matching score; the value measurement layer is used for quantifying contributions of different advertisement contacts to final conversion; and the execution layer is used for carrying out advertisement rendering and putting.

A method, an apparatus, and a computer-readable storage medium for searching of electronic documents. A search query is encoded to form a search vector. The search query includes at least one search term and seeks information within a plurality of electronic documents. A knowledge graph structure is generated using at least one search term. The search term is associated with at least another term in a plurality of terms in the knowledge graph structure. The search query is modified using the other term to generate a modified search vector. A response to the search query is generated based on one or more responsive document vectors retrieved from the plurality of electronic documents and that are semantically similar to the modified search vector.

The objective of the present disclosure is to search for content in a content streaming system, and an operating method of a server may comprise the steps of: acquiring a search word; using a language model trained on the basis of synopsis information included in metadata of content items, so as to determine a first vector corresponding to the search word; determining similarity between the search word and a first content item on the basis of the first vector corresponding to the search word and a second vector of the first content item; and providing a content search list including information about at least one content item including the first content item selected on the basis of the similarity.

An electronic data analysis method including at least one server, the electronic data analysis method comprising the steps of: a) Receiving, from an electronic device associated with a user to an electronic data analysis system, a search query including one or more search query terms; b) Maintaining an electronic search term database in association with the electronic data analysis system, the search term database comprising a plurality of search terms; c) Identifying, using the electronic data analysis system, one or more relevant search terms from the plurality of search terms in the electronic search term database based on the one of more search query terms; d) Sending, using the electronic data analysis system, a search request related to the one or more relevant search terms to a plurality of data sources external to the electronic data system; e) Receiving, using the electronic data system, data associated with the one or more relevant search terms from the plurality of data sources; f) Assigning, using the electronic data system, the data retrieved from the plurality of data sources into a plurality of data visualisation categories; and g) Displaying, using the electronic data system, the plurality of data visualisation categories on the electronic device.

A method for voice-based interaction between a user (e.g., a shopper) and online stores includes capturing an initial voice input from a user and converting the voice input to text. The text is analyzed and initial search terms are created such as product name, product description, product category, product brand name, product manufacturer and retailer. One or more searches are conducted using the initial search terms to identify one or more retailers meeting the search terms. One or more identified online retailers are accessed and searches are executed at the one or more identified retailers, generating search results. The identified online retailers have a user interface allowing purchases of the identified product or item. The search results are returned to the user.

The invention provides a question answering method and device based on a large model, computer equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps that at least one target plug-in is determined from a plurality of candidate plug-ins based on input questions and search words of the candidate plug-ins associated with a large model, and the search word of each candidate plug-in is used for indicating question features used for requesting corresponding services; the matching degree between the search word of the at least one target plug-in and the question meets a matching condition; based on the at least one target plug-in, calling a service interface of a service corresponding to the at least one target plug-in, and based on the service interface corresponding to the at least one target plug-in, obtaining service data from a server side corresponding to the at least one target plug-in; and processing the service data corresponding to the at least one target plug-in through the large model to obtain an answer to the question, and outputting the answer. According to the method, the problem processing efficiency is improved on the basis of ensuring that the large model can accurately process the problem.

To improve usability of a user.SOLUTION: The document information includes a document display name of a document file corresponding to a search condition, a list of detected text sentences including a search word and characters before and after the search word in a text file, most frequent image data corresponding to a page in which the search word is detected most frequently, and a jump button that can be linked to a document address.SELECTED DRAWING: Figure 10

A search engine of a data exchange may receive a query comprising a set of search terms, retrieve a plurality of data listings based on the search terms of the query, compare a first embedding generated by a large language model (LLM) from the search query to second embeddings generated by the LLM for each of the plurality of data listings to determine a respective relevance for each of the plurality of data listings to the search query, and rank the plurality of data listings based on the respective relevance for each of the plurality of data listings to the search query.

A system generates domain-expert agents for a particular domain. The system generates a hierarchy of topics for the domain by forming structured query inputs and requesting a machine learning-based language model to produce topics and associated sets of search terms. A knowledge store is built for the domain by identifying, for each topic, facts represented symbolically and / or in natural language. For at least one topic, programs comprising instructions for executing domain-specific procedures are generated and stored as symbolic and / or natural language programs. The knowledge store is evaluated to determine an expertise level for a domain-expert agent. If the knowledge store meets a threshold of knowledge, a deployment package is created comprising the knowledge store, and the domain-expert agent is deployed. The deployed agent utilizes the knowledge store together with a machine learning language model to address and solve problems specific to the domain.

To improve usability of a user.SOLUTION: When a search condition including at least one search word is supplied from a user terminal, searching a local government document database for a document disclosed by a local government, searching for the search word in the text file corresponding to a document ID matching the search condition, and generating result image data for displaying a list of document information in which a document display name including the search word, a detected text sentence including the search word and characters before and after the search word in the text file, and a document link button including a link to the document file are displayed in one frame.SELECTED DRAWING: Figure 1

The embodiment of the application discloses a kind of association word display method, terminal equipment and computer storage medium, the method includes obtaining search column input search word;Determine the association word corresponding to search word;Determine the application corresponding to the association word;Association word and the mark corresponding to association word are shown, mark is used to open the application corresponding to the corresponding association word of association word.This application provides the association word display method can enhance the interaction of user in the search engine of terminal equipment when searching with other applications on terminal equipment, so that user obtains more rich, professional experience.

The invention discloses a search result display method, computer equipment and a storage medium, and belongs to the technical field of computers. Comprising the following steps: extracting a search term to obtain a first entity associated with the search term, extracting information of a commodity title of any target commodity in a plurality of target commodities to obtain an associated second entity set, and according to a relationship between the entities in the second entity set and the first entity, extracting the information of the commodity title of any target commodity in the plurality of target commodities; querying in the target knowledge graph to obtain a third entity set; determining the correlation between the search term and the target commodity according to the correlation score between each entity in the third entity set and each entity in the second entity set; and sorting the plurality of target commodities on the basis of a sequence from high to low of the correlation between each target commodity in the plurality of target commodities and the search word. Therefore, the correlation between the search word and the search result can be accurately calculated, so that the search result can be accurately displayed, and the user experience can be improved.

An information search method includes: obtaining search words at least including a question to be searched and obtaining an initial text vector representation of the search words; obtaining a video corresponding to the search words, and obtaining multi-modality vector representations of the video; starting from the initial text vector representation, performing N rounds of interaction between the video and the search words based on the multi-modality vector representations and a text vector representation of the search words of a current round, to generate a target fusion vector representation, where N is an integer greater than or equal to 1; and obtaining target video frames matching the question to be searched by annotating the video based on the target fusion vector representation.

A system for contextual matching video content based on multimodal metadata extraction generated by processing one or more scenes to extract metadata corresponding to multiple extraction modes, and an embedding model for each extraction mode wherein an aggregated embedding model responsive to said metadata embeddings for each mode formulates an aggregated embedding with an embedding extractor responsive to a text input with an embedding model coordinated with said embedding model wherein said embeddings are in the form of a vector, and a vector comparison processor for determining the distance between the query vector and a vector representing the aggregated embedding. The coordination between embedding models is established by training. The embedding extractor may accept a free-form text query and present one or more subqueries for embedding. A textual inversion engine may be provided to generate an image from the embeddings to provide feedback to a user. In this way a user can confirm the effectiveness of the text query. A text editor may be provided for a user to enter and to edit a query.

The application discloses an entity classification method and an electronic device, and relates to the technical field of natural language processing, and comprises the following steps: an electronic device acquires input content in a human-computer interaction scene. The electronic device performs entity recognition on the input content, and acquires an entity category corresponding to an entity in the input content. If it is found that one entity corresponds to multiple entity categories, the electronic device determines one target entity category corresponding to the entity based on the probability that the entity belongs to each entity category. The electronic device performs a response to the input content based on the target entity category of the entity. The probability that the entity belongs to the entity category comprises a first probability representing the appearance of the entity in the entity category in search results obtained by searching a search engine with the entity as a search word, and / or a second probability representing the response of the entity in the entity category in user feedback data and the selection of the entity by a user. The target entity category determined based on the first probability and / or the second probability is more accurate, and the entity classification is more accurate.

Method of identifying computing products from a search query, including receiving a search query including a word string; analyzing the search query to determine that the word string does not refer to a particular computing product; in response to determining that the word string of the search query does not refer to a particular computing product, identifying words of the word string; identifying, from an index, a plurality of computing components based on the words of the word string; generating a plurality of search terms, each search term based on a differing combination of one or more computing components of the plurality of computing components and a word of the words of the word string; and identifying, from the index, one or more computing products based on the plurality of search terms

An information processing device that assists in recalling disease names using the results of analysis of examination images. [Solution] An information processing device is provided, comprising: an analysis unit that obtains the results of analyzing an examination image; a first output unit that outputs a search term using a string of words that combines the location of the abnormal finding obtained from the analysis results of the analysis unit and at least one of the content or size of the abnormal finding; a search unit that searches a database for information on disease names or disease names using the search term output by the first output unit; and a presentation unit that presents disease names or disease names based on the search results from the search unit.

The present application relates to the field of commodity retrieval technology, and in particular to a commodity retrieval method and device, the method comprising obtaining user information and a current search term from a user terminal; predicting the user's intent based on the user information in combination with a preset intent recognition model to obtain a prediction result; determining the part of speech, search scope, and initial weight of the current search term based on the prediction result; searching for recommended commodities within the search scope based on the current search term; calculating the rating of each recommended commodity based on the initial weight; and sorting each recommended commodity in descending order of rating value and pushing the result to the user terminal. The present application can achieve the purpose of recommending commodities required by users more quickly, which is conducive to improving the user's browsing experience and increasing the order rate of commodities.

1. The name of the design product: the display area of the information search graphical user interface of the electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the partial graphical user interface in the electronic device. 4. The picture or photo that best shows the design points: design 1 change state diagram 1. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the graphical user interface is used to search for information. The partial graphical user interface protected by the design is used to display search information and recommend related search words. 7. The human-computer interaction mode of the graphical user interface: in response to the user touching or clicking the page content of the main view of design 1, the graphical user interface changes from design 1 main view to design 1 change state diagram 1 to display the selected page content and related recommended words; in response to the user touching or clicking a recommended word in design 1 change state diagram 1, the graphical user interface changes from design 1 change state diagram 1 to design 1 change state diagram 2 to display the search results of the recommended word. In response to the user touching or clicking the page content of the main view of design 2, the graphical user interface changes from design 2 main view to design 2 change state diagram 1 to display the selected page content and related recommended words; in response to the user touching or clicking a recommended word in design 2 change state diagram 1, the graphical user interface changes from design 2 change state diagram 1 to design 2 change state diagram 2 to display the search results of the recommended word. 8. Other circumstances that need to be explained: the part of the graphical user interface shown by the dashed line in each view of design 1 to design 2 does not constitute the part protected by the design.

The application discloses a resource putting word mining method and device, electronic equipment and a storage medium, relates to the technical field of computers, and particularly relates to the field of artificial intelligence such as deep learning and large models. The specific implementation scheme is as follows: a candidate historical search word is acquired, and the candidate historical search word is clustered to obtain a candidate search word set; a plurality of search word samples are extracted from the candidate search word set; a large model is used to perform intent determination on the search word samples, and an intent determination result of the search word samples is determined; wherein the intent determination result is used to indicate whether the search word samples meet a resource putting intent of a target product; the candidate search word set is filtered according to the intent determination results of the plurality of search word samples, and a retained target search word set is obtained; and a target putting word is determined from the target search word set.

The present disclosure relates to a server and a push method of recommended content, and relates to the technical field of data recommendation. The server comprises: obtaining a search keyword of an event, a search topic corresponding to the search keyword, and first sorting information of the search keyword; determining a preferred search topic of the event according to the first sorting information and the search topic corresponding to the search keyword; determining a target search keyword from the search keyword according to the preferred search topic; determining target recommended content corresponding to the target search keyword; and sending the target recommended content to a terminal. The embodiments of the present disclosure can accurately recommend content of interest to a user, and improve the recommendation effect and user experience.

The application relates to a search quality optimization method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining at least one target search record, the target search record comprising a target search word and search result content features, the correlation degree between the search result content features and the target search word satisfying a first correlation condition, the correlation degree being evaluated by a preset correlation scoring model; selecting at least one to-be-reviewed search record satisfying a second correlation condition from the target search records; for any to-be-reviewed search record, performing correlation review on the target search word and the search result content features to obtain a correlation review result; in the case that the correlation review result does not satisfy the first correlation condition, constructing a negative sample based on the target search record and the correlation review result, the negative sample being used for optimizing the preset correlation scoring model. The method can improve the search quality optimization effect.