167 results about "Semantic relationship" patented technology

The invention provides an analyzable anti-attack network security method and system based on an AI unified model. The method comprises the following steps: S1, carrying out attack source tracing and attack mode identification on an input data stream; s2, performing protocol structure analysis and grammar element extraction on the input data stream in a grammar verification layer, and starting a grammar rule matching process to obtain a grammar exception perception set; s3, fusing attack vector information on the basis of a grammar anomaly perception set in a semantic analysis layer, constructing a semantic relation graph, and outputting a semantic risk vector; s4, taking the semantic risk vector as input, combining a business scene, resource constraint and strategy preference, modeling a defense target, and outputting an optimal response path; and S5, forming a model evolution path based on local feedback and global collaboration. Through a three-layer full-information analysis mechanism and behavior feedback driving, interpretable recognition of attack intentions and collaborative optimization of defense paths are realized, attack recognition is comprehensive, response decision is accurate, and strategy evolution is controllable.

The invention relates to the technical field of artificial intelligence, and provides an intelligent dialogue method, system and device based on big language model illusion alleviation and a medium, and the method comprises the steps: outputting multiple rounds of heuristic answer results, stimulation thinking results and self-reflection results based on an input question through a big language model; generating a reasoning chain based on the heuristic answer result, the stimulation thinking result and the self-reflection result of the same round; performing logic semantic relationship detection on each reasoning chain through a small language model to obtain a target reasoning chain with a correct relationship; and extracting and outputting a target answer of the input question from the target reasoning chain. According to the intelligent dialogue method based on big language model illusion relief, the big language model is used for forming an inference chain, the small language model is used for analyzing the output of the big language model, inaccurate information can be accurately recognized and filtered out, staged cooperation between the two models is achieved, the limitation that the big language model conducts illusion detection in a self-reflection mode is overcome, and the intelligent dialogue efficiency is improved. And a result can be accurately output.

The invention discloses a lightweight multi-modal content recognition system based on a double-track migration framework, and relates to the technical field of content recognition, and the system comprises a data collection module which is used for synchronously collecting multi-source content of a text and an image and carrying out standardization processing and tensor construction to form a fusion tensor X; and the model construction module is used for inputting the fusion tensor into a dual-track migration structure constructed based on a Transform backbone network, and the dual-track migration structure realizes task semantic alignment and structure migration under parameter freezing through Prompt Learning embedding and Adapter-Tuning insertion, and outputs an intermediate representation of modal alignment. According to the method, the training and deployment cost of multi-modal content recognition is remarkably reduced, the semantic expression ability in the modal is enhanced, the cross-modal alignment precision and fusion depth are effectively improved, and the perception and recognition ability of the model to the complex semantic relationship is enhanced.

The invention discloses a large model defense method based on multi-view image-text conversion, and relates to the technical field of artificial intelligence security. The method comprises the steps of obtaining an image-text dialogue prompt word text, extracting syntactic structure parameters and semantic parameters of statements, splicing the syntactic structure parameters and the semantic parameters into a multi-modal tensor, and expanding a spliced vector into a plurality of structure vectors according to semantic relationship density to form a semantic structure vector group. According to the method, semantic density splitting and image space mapping of the multi-modal tensor are utilized, the cross-modal correlation analysis capability is enhanced, the image-text mixed attack path is accurately identified, and the entity mapping relation between the pixel gradient and the regional mask is combined, so that two-way verification of image-text semantic consistency is realized, and semantic fault vulnerabilities of single-modal detection are eliminated; a path intensity and instruction word dynamic weight fusion mechanism is adopted, the abnormal risk level of a statement structure is quantified, the adaptive limitation of a static rule to semantic variation is broken through, and the illegal content output probability is reduced.

The invention belongs to the technical field of cross-modal information retrieval, and provides a quick cross-modal retrieval method and system fused with fine-grained semanteme, multi-modal data to be retrieved are obtained, a trained cross-modal retrieval model is used for learning to obtain continuous Hash features, discrete Hash codes are generated according to the continuous Hash features, and the multi-modal data to be retrieved are retrieved according to the discrete Hash codes. Performing cross-modal matching and retrieval based on Hamming distance matching, and feeding back a retrieval result; in the training process of the cross-modal retrieval model, the fusion loss function, the cross-modal alignment loss function and the shared subspace loss function are subjected to weighted fusion, and corresponding parameters are optimized with the purpose of minimizing the total loss function after weighted fusion until training requirements are met. According to the method, the cross-modal semantic consistency among different modal features is optimized, so that the generated Hash code can reflect semantic association among multi-modal data more accurately, and the precision and efficiency of cross-modal retrieval are improved.

The invention discloses a camouflage target detection method and system based on a double-domain fusion enhanced network, and relates to the technical field of target detection. Through the nonlinear double-domain fusion module, in combination with nonlinear mapping of a spatial domain and a frequency domain, key difference characteristics of a frequency domain amplitude spectrum and a phase spectrum are captured, the problem that the detection performance is reduced in a scene of low contrast and the like depending on an RGB spatial domain is solved, and the target discrimination degree is improved; based on a lightweight scale perception modulation converter and a double-feature fusion module, multi-scale features are extracted, aligned and fused, a semantic relation is integrated by means of cross attention, and the problems of detail loss and boundary fuzziness caused by scale diversity are solved; the context feature enhancement module integrates cross attention and edge auxiliary injection, accumulates multi-layer feature integration, gives consideration to a global boundary and a local structure, effectively reduces false detection, missing detection and edge roughness, and further enhances robustness through multi-layer auxiliary supervision.

The invention provides an enterprise knowledge graph automatic construction and intelligent retrieval method, which comprises the following steps: collecting multi-source data from a heterogeneous enterprise information system, and carrying out data cleaning and standardization processing; on the basis of a comprehensive scoring mechanism of field similarity and behavior semantic vectors, entities from different systems are merged, and a standard entity set with a unique identifier is generated; based on the standard entity set, in combination with a scoring mechanism of a task-type relationship and a collaborative relationship, extracting a semantic relationship from a behavior record, and constructing an enterprise knowledge graph structure; extracting representative semantic paths from the knowledge graph structure, screening high-quality paths through a path scoring model, and organizing the high-quality paths into a structured path index set; and receiving a natural language query statement, encoding the natural language query statement into a semantic vector, matching the semantic vector with the path index set, executing query in the atlas in combination with an authority control mechanism, and returning a result.

The invention discloses a multi-modal video sequence segmentation method based on a multi-scale codec. The method comprises the following steps: extracting image features and text features; joint feature representation containing image and language semantic information at the same time is obtained; extracting a multi-scale fusion feature sequence under different spatial resolutions; obtaining a feature representation sequence after space-time modeling; cross-scale fusion feature representation in a unified semantic space is obtained; foreground features are obtained; and performing visual visualization on the segmentation mask to generate a semantic segmentation map. According to the multi-modal video sequence segmentation method, deep interaction of image and language semantics can be realized, a context and semantic relationship is established, cross-modal information interaction is introduced through a multi-modal cooperation mechanism, the robustness and stability of a model in a complex dynamic scene are enhanced, and the segmentation efficiency is improved. And the segmentation effect and generalization ability of the image sequence segmentation model in the segmentation task are effectively improved.

The invention provides an extensible remote sensing deep learning sample library construction method based on target region planning, which is applied to the technical field of remote sensing image processing, and comprises the following steps: determining target region ranges of a plurality of target regions based on image resolution and a plurality of target region center points; performing cutting and coding naming on the target region remote sensing image based on the vector boundary of the target region range and a preset coding rule to obtain coarse samples corresponding to the plurality of target regions respectively; inputting the coarse sample subjected to feature enhancement processing into a large language model to obtain corpus data of the coarse sample output by the large language model; performing feature alignment with the coarse sample based on the semantic relationship to obtain a feature semantic mapping result of the coarse sample; constructing a coarse sample knowledge graph based on the semantic relationship information and the feature semantic mapping result; and inputting the coarse sample knowledge graph into the open vocabulary target detection model to obtain potential samples output by the open vocabulary target detection model. According to the invention, dynamic extensible labeling of large-scale remote sensing samples can be realized.

The invention relates to the technical field of text processing, in particular to a text classification method and system based on semantic analysis, and the method comprises the following steps: segmenting semantic units, constructing a direction change sequence, positioning mutation nodes, generating a consistency section, forming a convergence section, and outputting a classification result. According to the method, a continuous change sequence is formed by constructing a semantic embedding vector and calculating a direction difference, a semantic mutation point can be anchored and divided into sections by combining mutation intensity identification and local jump tracking, and a semantic closed structure and a convergence section are extracted by means of context direction consistency judgment and generic label comparison; precise recognition of a semantic relation chain is realized, semantic jump and conflict starting points can be dynamically sensed, the semantic boundary recognition capability is improved, and the understanding and classification capability of a model on semantic attribution in a complex context is enhanced on the premise of not depending on a fixed dictionary and shallow statistics. The problems that a traditional model is slow in response to an abrupt change structure and weak in semantic convergence recognition are effectively solved.

The invention discloses an anti-fact multi-mode dialogue emotion causal reasoning method based on a double-branch hypergraph. The method comprises the following steps: respectively extracting sentence level feature vectors of three modes of text, voice and vision from input multi-mode dialogue data; carrying out modeling on a high-order relationship in the modals and between the modals by utilizing a hypergraph structure, and constructing a dialogue hypergraph containing multi-modal nodes and emotion nodes; introducing a hypergraph attention network on the hypergraph, learning contribution weight of each modal node to a target emotion node, and selecting a candidate reason node set; the candidate reason nodes are intervened, an anti-fact branch is constructed, a fact situation and final node feature representation under the anti-fact situation are calculated, and a causal effect vector is obtained; and designing a joint optimization objective function, and carrying out joint training on emotion recognition loss and causal consistency loss to realize synchronous prediction of emotion categories and emotion reasons. According to the method, a high-order semantic relationship can be effectively modeled in a multi-modal dialogue scene, and a key reason for emotion formation is reasoned.

The invention provides a culture symbol transmission method and system based on Sichuan opera facial makeup art patterns, and particularly relates to the field of facial makeup digital transmission. According to the method, multi-level modeling of a Sichuan opera facial makeup pattern from a pixel layer to a component layer and then to a semantic layer is realized by fusing image processing, component contour matching, image structure modeling and an image neural network technology; according to the method, main and auxiliary color block areas and component information in an image can be automatically identified and structurally stored, a spatial semantic relationship of the image is learned through a graph neural network, a structural semantic code is output, and finally a multi-dimensional cultural label index capable of being filed, clustered and classified is generated; the method effectively overcomes the limitation that in the prior art, only stays at an image level and is lack of structure and semantic modeling, enables the Sichuan opera facial makeup pattern to have machine-readable component structure representation and semantic feature representation, and lays a foundation for cultural inheritance and intelligent propagation.

The invention relates to a multi-modal knowledge representation learning method fusing a multi-attention mechanism and semantic enhancement, and aims to solve the problems that the multi-modal knowledge representation method is insufficient in multi-modal feature fusion and difficult to effectively model complex semantic relationships such as symmetric and anti-symmetric. The method comprises the following steps: firstly, extracting entity image and text features from a multi-modal knowledge graph by using a CLIP model, and extracting entity audio features by using a VGGish model; then image features are enhanced through spatial attention, and image-text feature fusion is realized through cross attention; designing a cross-modal fusion module to dynamically fuse the image-text features and the audio features to obtain multi-modal fusion features; and finally, extracting structured features based on a ComplEx model, integrating the structured features with multi-modal fusion features through an adaptive dual-channel scoring function, and optimizing a training process by adopting a contrast learning loss function. The multi-modal features can be fully fused, the semantic expression ability is enhanced, and the performance of downstream tasks such as intelligent question and answer is improved.

The invention discloses an AI medical information accurate retrieval method based on a knowledge graph, and belongs to the field of medical information, and the method comprises the steps: collecting and integrating multi-source medical data, employing an entity recognition and relation extraction technology, accurately extracting medical entities and semantic relations, and constructing a complete and accurate medical knowledge graph; according to the method, the medical knowledge graph is constructed, query is converted into a query sub-graph structure in combination with a natural language processing technology, and the FFGR algorithm is applied to fuse attributes, structures and semantic features of nodes to calculate a comprehensive score, so that the correlation between the nodes and the query can be accurately evaluated; the potential relationship can be deeply mined when the complex disease query is processed; the DKU algorithm utilizes a deep learning model to carry out feature extraction and relation prediction on new data, and the structure and content of the knowledge graph are updated in real time. And new diseases, medicines and relationships can be timely reflected in the knowledge graph, so that the retrieval result is always based on the latest medical knowledge.

The invention relates to the technical field of artificial intelligence, and provides a multi-modal data fusion modeling method based on multi-task learning, and the method comprises the steps: respectively extracting image and text features in data, and carrying out the fusion to obtain first fusion data; respectively inputting the fused data into a Transform decoder and at least two weight layers for processing, and fusing the fused data with the original fused data again through a self-adaptive gating layer to obtain second fused data; and finally, based on the second fusion data, respectively calculating classification loss and comparison loss, and when the two types of loss meet preset conditions, outputting a final multi-modal data fusion model. The invention further discloses a system. According to the method and the system, the semantic alignment between the image and the text is enhanced, the cross-modal semantic problem is effectively solved, and the understanding ability of the model to the complex semantic relationship is improved. The model is enhanced to have adaptability in different task scenes, the generalization ability and robustness of the model are remarkably improved, and data noise is reduced.

The invention discloses a sparse view angle three-dimensional Gaussian language field construction method and system for humanoid robot grabbing. Comprising the following steps: acquiring an RGB image set and a user natural language instruction under a sparse view angle; reconstructing a three-dimensional point cloud through stereo matching, and initializing a three-dimensional Gaussian primitive field after noise elimination and measurement alignment; two-dimensional semantic features are embedded into the field, joint optimization is carried out through a double-path semantic supervision module, and a three-dimensional Gaussian language field with consistent semantics is constructed; wherein the dual-path semantic supervision comprises an object perception path and a global context path, the local semantic consistency and the overall semantic relationship are constrained respectively, and the final semantic representation of each Gaussian primitive is obtained through weighted fusion; and finally, candidate grabbing postures are generated, semantic reordering is carried out in combination with a language instruction, and the optimal grabbing posture is screened through geometric-semantic joint scoring. Under the sparse image set input condition, the execution accuracy of a robot grabbing task under a complex instruction can be improved.

The invention discloses an intelligent dialogue memory management method and system based on the logistics field, and relates to the technical field of logistics intelligent dialogues, and the method comprises the steps: obtaining the historical logistics dialogue data of a target user, generating a user feature-interactive entity-semantic relationship initial knowledge graph based on a QWen2.5-32B large language model, and constructing a Milvus semantic vector library; monitoring a logistics dialogue flow in real time, and starting an adaptive memory management mechanism by taking three rounds of dialogue as a judgment threshold value; extracting user dialogue content attribute features and behavior preferences, and dynamically updating a user feature-interactive entity-semantic relationship initial knowledge graph and a Milvus semantic vector library; and triggering a logistics dialogue according to a target user, starting a multi-modal memory recall mechanism, cooperatively retrieving a user feature-interactive entity-semantic relationship knowledge graph and a Milvus semantic vector library, generating a personalized intelligent dialogue response, and realizing a logistics intelligent dialogue memory management closed loop. The beneficial effect of the invention is that the intelligent and personalized capabilities of the system are enhanced.

The invention discloses a coal geology field sentence semantic embedding modeling method based on multi-task learning. The method comprises the following steps of coal geology corpus collection and preprocessing; constructing and marking a geological entity dictionary, and generating a geological domain exclusive entity data set; establishing a multi-task learning model structure based on a Chinese BERT model; performing sentence vector generation vector extraction and evaluation on statements in the coal geology field by using the trained model; the multi-task learning-based model provided by the invention is applied to a downstream scene. According to the coal geology field sentence semantic embedding modeling method based on multi-task learning, sentence vector modeling and entity recognition tasks are jointly optimized, so that the model can more accurately understand the semantic relation between key metal elements and minerals in coal, sentence representation with more field semantic features is output, and the sentence semantic embedding modeling efficiency is improved. And the understanding, classification and knowledge expression capabilities of the coal geology text are improved.

This invention discloses a lightweight multimodal content recognition system based on a dual-track transfer framework, belonging to the field of content recognition technology. It includes a data acquisition module for simultaneously acquiring multi-source content from text and images, performing standardization processing and tensor construction to form a fused tensor X; and a model construction module for inputting the fused tensor into a dual-track transfer structure built on a Transformer backbone network. This dual-track transfer structure achieves task semantic alignment and structural transfer under parameter freezing through Prompt Learning embedding and Adapter-Tuning insertion, outputting an intermediate representation of modality alignment. This invention significantly reduces the training and deployment costs of multimodal content recognition, enhances the semantic expression capabilities within modalities, effectively improves cross-modal alignment accuracy and fusion depth, and enhances the model's ability to perceive and recognize complex semantic relationships.

The present invention discloses a cross-modal image text retrieval method based on deep learning, and proposes a novel cross-modal feature extraction and alignment framework. By learning the semantic representation of images and texts in a common feature space, efficient cross-modal retrieval is performed. The framework includes multiple modules. In the feature extraction stage, BERT Tokenizer and BERT Embedding are used for text encoding, and Faster R-CNN and ResNet-101 models are combined for image feature extraction to ensure the dimensional consistency of image and text features. In the feature alignment stage, fine-grained semantic alignment of image and text features is achieved through I2T Attention and T2I Attention modules, which significantly improves the accuracy of cross-modal matching. In the relevance scoring stage, the relevance scoring matrix is ​​generated by calculating the similarity scores between image features and text features, and the feature alignment effect is further optimized through normalization and attention redistribution. In the optimization stage, the system innovatively adopts the shared semantics and ranking loss strategy, and integrates the OpenCLIP model framework. By contrastive learning, the semantic relationship between images and texts is efficiently mined from large-scale unlabeled data, showing strong transfer learning capabilities and inference accuracy.

The invention discloses a method and a system for searching by combining user input information, and particularly relates to the technical field of language paragraption.The method comprises the following steps: firstly, converting a plurality of user inputs into an input semantic structure containing keywords, semantic relationships and context tags; comparing the structures, extracting interactive focuses and generating a semantic consensus score; constructing a fusion path diagram according to the consensus score, the keyword weight and the semantic adaptation degree; semantic fusion and conflict mediation are executed according to the path diagram, and a unified expression structure is generated; and finally, sorting the keywords according to semantic levels and task orientation, and constructing a comprehensive search expression to finish query output. According to the method, through semantic analysis and cross comparison input by multiple users, the consensus content is extracted, the fusion path diagram is constructed, and it is ensured that the fusion sequence is reasonable and semantic consistency is achieved; and finally, a comprehensive semantic expression structure is generated and a search expression is constructed, so that accurate alignment of query contents and user intentions is realized, and representativeness and accuracy of search results are improved.

The invention provides an intelligent processing method and system from structured data to a text based on a natural language, and relates to the technical field of data processing.The method comprises the steps that the text chunk analysis process is optimized and adjusted according to analysis optimization parameters, sentences are segmented into non-overlapping phrases with syntactic function labels, and the text after chunk analysis is obtained; performing syntactic and semantic structure analysis on the text subjected to block analysis, and establishing a semantic association relationship between internal structures of sentences through component analysis, dependency analysis and semantic dependency graph analysis to obtain structured semantic information; performing multi-sentence logic association analysis on the structured semantic information on a chapter level to obtain semantic information of the whole chapter; and based on the semantic information of the overall chapter, obtaining a target natural language text by utilizing a pre-trained large language model. According to the method, the accuracy and fluency of conversion from the structured data to the text are improved.

The invention discloses a method for reducing large model illusion of a question answering system based on knowledge graph representation learning, which comprises the following steps of: firstly, based on Ollivier-Ricci curvature calculation, embedding different structures in a knowledge graph into a plurality of approximate geometric spaces so as to analyze geometric patterns in data; secondly, generating a new entity representation by aggregating neighbor information, and transmitting and fusing information in different geometric spaces by using indexes; and finally, a dynamic curvature adaptive adjustment strategy is adopted to promote cooperative training and efficient fusion of multi-geometric space representation. According to the method, a complex semantic relationship is accurately described by combining geometric space and knowledge graph representation learning, geometric distortion in an embedding process is effectively reduced, and a structured knowledge system is constructed. According to the method, the accuracy and credibility of a large model in knowledge reasoning are improved, the illusion problem caused by incomplete or contradictory knowledge representation is solved, and powerful support is provided for a question and answer system.

Techniques for performing contextualized intent and slot extraction using a large language model (LLM) are disclosed. The LLM is generally pre-trained on an arbitrary corpus of language training data. A prompt is provided to the LLM. This prompt includes a limited number of prompt phrases. The prompt phrases share a semantic relationship with one another. A spoken utterance is recorded and then converted to text, resulting in generation of a transcription. The transcription is provided to the LLM. The LLM extracts, from the transcription, an extracted intent and an extracted slot. The extracted intent is determined to be related to a prompt-described intent that was included in the prompt. The prompt is supplemented by adding the extracted intent and the extracted slot to the prompt, resulting in the extracted intent being identified as sharing the semantic relationship with the other prompt phrases in the prompt.

The invention discloses a knowledge graph semantic path guided remote sensing data recommendation method, which introduces a representation learning mechanism based on knowledge graph semantic path guidance, and realizes modeling of structured semantic association between nodes by constructing Meta-Path with task semantics. According to the method, a path-oriented random walk strategy is designed by combining a Meta-Path2Vec embedding method, semantic representation in an embedding space is obtained, and a sorting recommendation result is calculated and generated based on path similarity. According to the method, computable modeling of the semantic chain in the knowledge graph structure is realized, and path controllability, semantic interpretability and structural universality in the recommendation process are enhanced. According to the method, a complex semantic relationship among a natural disaster type, a remote sensing application task and remote sensing data can be modeled through meta-path constraint in a multi-source heterogeneous environment. In combination with a Meta-Path2Vec embedding method and a random walk strategy, high-order semantic association modeling between tasks and remote sensing resources is realized, and potential relationships between entities are effectively captured.

The invention relates to the technical field of multi-modal dense subtitle generation, in particular to a multi-modal dense subtitle generation method and system based on heterogeneous graph convolution enhancement, and the method comprises the steps: obtaining image data, and extracting visual features and position codes of a region of interest in an image through a multi-modal encoder; constructing a multi-modal heterogeneous graph based on the regional features and the position codes, and defining three edge types of a spatial relationship, a semantic relationship and cross-modal interaction; performing multiple rounds of message passing and feature aggregation by adopting a hierarchical heterogeneous graph convolutional network to generate enhanced features; the enhanced features are input into a context relation modeling module, and representation containing rich context information is generated; the system comprises a data acquisition module, a heterogeneous graph construction module, a feature aggregation module and a subtitle generation module. According to the method, the multi-mode heterogeneous interaction capability can be improved, the accuracy of generated subtitle semantics is high, and the method can be applied to scenes such as automatic driving, virtual assistants and intelligent media analysis.

The invention belongs to the technical field of text generation, and particularly relates to a library literature intelligent recommendation system based on knowledge graph model generation, which comprises a model construction module, a recommendation module, an output module and a user interaction module, according to the library literature intelligent recommendation system generated based on the knowledge graph model, the model construction module is set to construct the knowledge graph model, and the knowledge graph model deeply understands complex semantic relationships among entities such as concepts, topics and authors in library literatures through an entity-relationship-entity structure instead of simple matching based on words, so that the knowledge graph model has the advantages that the knowledge graph model is simple in structure and easy to implement, and the library literature recommendation efficiency is improved. In addition, the knowledge graph model can mine abundant association relationships among the literatures, such as reference relationships, cooperation relationships and theme hierarchical relationships, and the knowledge graph model shows that the knowledge graph can find that an author of one literature and a manuscript reviewer of another literature are the same potential association, so that related literatures are recommended for the user.

The invention discloses a four-dimensional index-based scientific and technical literature knowledge automatic labeling method and system, and relates to the technical field of natural language processing, and the method comprises the following steps: constructing a four-dimensional index architecture; the method comprises the following steps: receiving multi-format scientific and technical literatures through a data input layer, and performing index analysis to obtain a document layer semantic structure, a paragraph layer semantic relation graph, a sentence semantic relation network and an entity semantic relation; performing semantic density calculation and knowledge enrichment region recognition to obtain a knowledge enrichment region recognition result; and establishing a cross-hierarchy semantic association model, performing entity boundary prediction and multi-level labeling on a knowledge enrichment area recognition result to obtain a structured semantic labeling result, and performing visual output and display. The technical problem that in the prior art, scientific and technical literature knowledge recognition is not accurate, hierarchical labeling is insufficient, and consequently the literature processing efficiency and accuracy are low is solved, and the technical effects that precise recognition and multi-hierarchical labeling of scientific and technical literature knowledge are achieved, and the literature processing efficiency and accuracy are improved are achieved.

Techniques for performing contextualized intent and slot extraction using a large language model (LLM) are disclosed. The LLM is generally pre-trained on an arbitrary corpus of language training data. A prompt is provided to the LLM. This prompt includes a limited number of prompt phrases. The prompt phrases share a semantic relationship with one another. A spoken utterance is recorded and then converted to text, resulting in generation of a transcription. The transcription is provided to the LLM. The LLM extracts, from the transcription, an extracted intent and an extracted slot. The extracted intent is determined to be related to a prompt-described intent that was included in the prompt. The prompt is supplemented by adding the extracted intent and the extracted slot to the prompt, resulting in the extracted intent being identified as sharing the semantic relationship with the other prompt phrases in the prompt.

Disclosed is a system and method for enhancing value for advertisers by helping them select and initiate the purchase of an advertisement associated with advertising (ad) words or phrases that have strong semantic relationships to a given context, but which are not necessarily the most popular ad words or phrases with the highest costs. Advertisements associated with ad words or phrases that have strong semantic relationships to a given context, and yet are still cost effective in that their calculated value exceeds the costs of purchasing the ad keywords, are bid for and bought. The system and method may be adapted to automatically purchase advertisements associated with ad words or phrases when they fall within a desired price range based on their calculated value. As the prices of advertisements associated with these words or phrases fluctuate over time based on their popularity, the automated bidding and buying of advertisements may be used to purchase advertisements associated with words or phrases at a price desirable to a given ad purchaser. By automatically purchasing such advertisements, the return on investment for an advertiser may be improved.