86 results about "Verbnoun" patented technology

The invention relates to the technical field of data processing, in particular to a method and system for processing data based on a large language model.The method comprises the following steps that based on inter-sentence punctuation positioning and part-of-speech tagging, sentence blocks are divided to generate functional partitions, themes and relational words are extracted to judge semantic chain starting points, a trigger index sequence is constructed, and logic jump points and breakpoint positions are recognized; and mapping the label structure to the language model output analysis deviation, and generating a label mapping combination list. According to the method, semantic turning nodes can be captured by analyzing the semantic direction change trend, the relation between the semantic turning nodes and verb and noun combinations is judged, the position of a trigger point of an actual information transfer effect is extracted, and the break point area of a semantic path is recognized through the positioning of key word starting and stopping blocks and the logical judgment of noun group cross combination; the path integrity has a clear fracture identifier, a traceable semantic mapping structure path is established in a language model, and the accuracy, coherence and hierarchy clearness of semantic reconstruction are enhanced.

The invention relates to the technical field of artificial intelligence, in particular to a family service method and system based on artificial intelligence, and the method comprises the following steps: obtaining a statement analysis action sequence and a task direction, collecting the feedback of a device to generate a state structure, judging an instruction trend, recombining a statement to generate a behavior chain, and disassembling an action recognition conflict to extract a main control path. Analyzing a behavior time sequence generation prominent trend, and adjusting a display sequence to generate a function scheme. According to the method, through semantic component extraction and equipment state mapping, the accuracy of instruction target recognition is improved, word order rearrangement and logic connection enhance the continuity of a behavior chain, a control path is optimized and sorted according to verb density and object association, the task scheduling precision is improved, and behavior time sequence analysis recognizes a high-frequency operation forward trend; according to the method, priority dynamic adjustment and rearrangement of recommended content in combination with instruction frequency and time sequence characteristics are realized, the response initiative and the service matching degree are enhanced, and collaborative optimization of semantic understanding, behavior prediction and content recommendation is integrally realized.

The invention relates to the technical field of intelligent question answering, in particular to a knowledge tracking and evidence labeling combined RAG question answering system which comprises a semantic analysis module, a statement positioning module, a knowledge path calibration module, an evidence index module and an enhanced question answering module. According to the method, through structural analysis of natural language input and extraction of clear verb semantics, index entities and time normalization expression, it is ensured that input statements have uniformity and processing consistency in the structural level, through a text segment screening strategy based on labels, the text scanning range is effectively limited, the index positioning precision and efficiency are improved, and the method is suitable for popularization and application. A causal node link with statistical significance in a text is clearly extracted, and index combination constraint and identification labeling are performed on key sentences in path nodes, so that the index cross coverage of candidate evidence fragments is enhanced, and the density of index integration in answers and the consistency of a logic sequence are improved.

Disclosed herein are methods and systems for generating and using a shared knowledge language. In one embodiment, a method comprises retrieving data generated as a result of at least one computing device accessing one or more applications from a set of applications; generating a computer model comprising a set of nodes where each node having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; when the processor receives a request: identifying a node that corresponds to the request; retrieving a pair of related nodes within the nodal data structure the processor identifying whether the relevance threshold has been satisfied using each respective node's identifier corresponding to the schema.

The invention relates to the technical field of government affair intelligence, in particular to a government affair application platform based on an artificial intelligence model, and the platform comprises a semantic recognition module, a tag affiliation module, a rule screening module, a path generation module and a path verification module. According to the method, the subject-called structure for recognizing noun phrases and approval verbs in the government affair approval text is adopted, semantic boundaries are defined, the structure recognition accuracy is enhanced, keyword and field item comparison adopts a field-level consistency mode, the tag affiliation precision is improved, field value comparison logic is introduced in rule node screening, and the accuracy of tag affiliation is improved. In path generation, a path structure with clear logic is established through sorting index and field dependence identification, field conflicts and path chaos are avoided, field value consistency verification ensures that path content is highly matched with an approval text, and it is guaranteed that the path is effective and available; automatic processing from text analysis and field affiliation to path verification is achieved, and the efficiency of the government affair approval process is improved.

The invention relates to the technical field of artificial intelligence education, in particular to a Russian spoken language practice personalized recommendation method and system based on artificial intelligence, and the method comprises the steps: 1, outputting a phoneme sequence with a timestamp through Russian automatic voice recognition; 2, collecting an exercise interruption position and repeated read-after behavior data; 3, generating a dynamic learner portrait; 4, mapping high-frequency errors in the learner portrait into abnormal path weights of map nodes; 5, a lattice tail error option and a non-matching body verb interference item are injected; 6, when the voice fluency attenuation of the learner exceeds a dynamic threshold value, the sentence complexity is reduced; and 7, calculating an error rate descent gradient based on the exercise completion data, and dynamically adjusting the abnormal path weight of the knowledge graph. Through audio stream analysis and syntax tree construction, the system can accurately identify errors of the learner in grammar, pronunciation and other aspects, and the learning efficiency is improved.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

A method for generating a knowledge map includes: extracting, as main entities, words by applying a key-phrase-extraction language model to the document; extracting a verb representing a relationship between main entities as a relational verb by performing dependency parsing on a sentence including the main entities; clustering the extracted relational verb into clusters to generate a relational verb dictionary; generating a knowledge triplet including a first entity and a second entity among the main entities and a cluster identifier indicating a relationship between the first entity and the second entity based on the relational verb dictionary; and generating the knowledge map based on the knowledge triplet.

This invention relates to the field of intelligent question answering technology, specifically to a RAG question answering system combining knowledge tracking and evidence annotation. The system includes a semantic parsing module, a sentence localization module, a knowledge path annotation module, an evidence indexing module, and an enhanced question answering module. This invention extracts explicit verb semantics, indicator entities, and time-normalized expressions through structured parsing of natural language input, ensuring structural uniformity and processing consistency of the input sentences. A tag-based text filtering strategy effectively limits the text scanning range, improving the accuracy and efficiency of indicator localization. It clearly extracts statistically significant causal links from the text, and enhances the cross-coverage of indicator fragments by imposing indicator combination constraints and labeling key sentences in path nodes, thereby improving the density and logical order consistency of indicator integration in the answer.

The invention provides a Mongolian lip language recognition method based on multi-modal training. The Mongolian lip language recognition method comprises the steps of S1, a preprocessing stage, S2, a training stage and S3, a reasoning stage. The method comprises the following steps: S1, a preprocessing stage: segmenting words in a language model training text into forms of stems, lattice suffixes and verbs except verbs; s2, a training stage: firstly, performing feature extraction on audio and video by using ResNet to obtain feature representation of each mode; the feature representation is then input into a Conformer encoder, and a multi-modal shared representation is extracted through deep fusion; s3, a reasoning stage: firstly, performing fine feature extraction on a video image through ResNet, and then inputting the extracted video features into a Conformer encoder for deep processing; and finally, accurately predicting the text content by using a forward S4D decoder. The audio information is fused in the training process, so that the system can capture more voice features from a multi-mode angle, and the fuzzy and uncertainty possibly caused by only depending on the video information are made up.

The invention relates to the technical field of brand sentiment analysis, in particular to an artificial intelligence-based brand monitoring system, which comprises a brand word path extraction module, a verb target positioning module, a continuous chain construction module, a stand turning detection module and an intelligent fusion judgment module. According to the method, the dependency structure and the syntactic relationship are introduced into the semantic path, so that the semantic association between the brand word and the verb can be identified and modeled in a more targeted manner, the judgment of the verb target is guided to be more semantic hierarchy and context dependency, and the time sequence connection of brand behaviors is effectively realized; the recognition accuracy of continuous brand behavior nodes in a text is improved, dynamic tracking of emotional standing is achieved by recognizing standing turning features in sentences, and standing fusion phenomena in user emotion expression are captured through sequential change of verb and emotion combinations in a path structure and switching of pointing objects, so that the emotion expression efficiency is improved. The information insight effect of emotional expression steering and behavior continuity in the brand monitoring process can be improved.

This invention relates to the field of information retrieval structure technology, specifically to a method and system for monitoring data during the investment promotion process in industrial parks. The method includes the following steps: extracting investment promotion text, identifying combinations of verbs and resource nouns, analyzing word order and semantic features, filtering action-driven segments, dividing structural sequences and identifying connection relationships, establishing semantic jump paths, reorganizing sentence order, and generating a data monitoring structure template for investment promotion in industrial parks. In this invention, by extracting verbs and resource nouns to construct sentence content, combining word order features and semantic tags to classify and filter segments, dividing structural sequences based on the position of resource nouns and merging content with consistent semantic direction, mapping connectors to jump paths to construct task behavior sequences, and realizing the logical organization and sequential reorganization of sentences, this generates a data expression method with traceability and structured features, improving the semantic recognition efficiency and data monitoring capabilities during the investment promotion process in industrial parks.

A method for anticipating actions includes receiving video data indicating an object in an environment, encoding a noun embedding using a noun encoder based on the video data, and encoding a verb embedding with a verb encoder based on the video data. The method also includes generating a verb anticipation associated with the object in the environment by processing the noun embedding using a verb decoder, generating a noun anticipation associated with the object in the environment by processing the verb embedding using a noun decoder, and generating an action anticipation by combining the verb anticipation and the noun anticipation.

The invention discloses a syntactic selection simulation method, system and equipment based on event structure and syntactic frequency interaction. The method belongs to the technical field of dynamic simulation of Chinese syntax generation, and comprises the following steps of: 1, analyzing a question element role relationship of an input event, and generating an event structure vector; 2, calling a syntactic frequency database, and obtaining the use frequency weight of the candidate sentence pattern; 3, calculating the verb semantic distance between the target event and the historical event; 4, dynamically generating a syntactic selection probability through interaction; 5, when the events are the same and verbs are multiplexed, the syntax selection probability is adjusted. Through multi-dimensional interactive modeling of the event structure, the syntactic frequency and the semantic information, more accurate and natural syntactic selection simulation conforming to human language behavior rules is realized, and the intelligent level and simulation authenticity of a language generation system are remarkably improved.

The present application provides a method, an apparatus, and a computer-readable storage medium for sample processing. The method includes: obtaining a first sample set; performing part-of-speech tagging on each sample in the first sample set based on NLP; performing NLP word vector conversion on M words to obtain M word vectors; screening the M word vectors to obtain N word vectors; determining N words corresponding to the N word vectors according to the N word vectors, where part of the N words are verbs in terms of part of speech, and the remaining part of the N words are non-verbs in terms of part of speech; determining at least one function corresponding to a first word according to the first word which is a verb in terms of part of speech among the N words and a first knowledge sub-graph; and determining at least one piece of data corresponding to a second word according to the second word which is a non-verb in terms of part of speech among the N words and a second knowledge sub-graph. In this way, it helps to improve the processing efficiency of requirement analysis and reduce the labor cost.

This invention discloses a slow-fast diversity-aware prototype learning method for egocentric action recognition. This method utilizes a part-to-prototype (P2P) module and a slow-fast contextualization (SFC) mechanism. In P2P, partial tokens obtained from each frame by the video converter are further interacted with several initial prototypes through diversity-aware attention, refining / extracting the semantic information of the interacting objects from the partial level to the prototype level. In SFC, slow and fast awareness attention is introduced to interact with the original prototypes to finely supplement semantic information. Three streaming features are aggregated using slow and fast gates, ultimately yielding classification results for nouns and verbs.

The present application discloses a method and apparatus for training a semantic vector extraction model and representing semantic vectors. The method for training the semantic vector extraction model includes: obtaining a conversation corpus from the same scenario as a downstream task corresponding to the semantic vector as a first pre-training text; performing secondary training on the trained BERT language network based on the first pre-training text to generate a BERT language network that focuses on nouns and verbs in the text; obtaining text classification data and generating a text similarity dataset based on the text classification data; and fine-tuning a multi-task classification network based on the text similarity dataset to generate a semantic vector extraction model.

A system is provided.SOLUTION: A system comprising: means for acquiring a text from a web page; means for estimating a reference source of a pronoun based on a context and inserting the reference source instead of the pronoun; means for replacing a homophone with a synonym in accordance with the context; means for adjusting an order of a verb, an object, and a subject; means for recursively repeating the above to increase a degree of the text; and means for storing and outputting the converted text.SELECTED DRAWING: Figure 1

The application belongs to the technical field of scientific and technological information processing, and provides a method and system for determining a cause-effect relationship based on a scientific and technological information analysis knowledge graph. The method comprises the following steps: determining a first verb, a second verb and a scientific and technological information text segment to be processed, performing vector conversion to obtain a first verb embedding vector, a second verb embedding vector and a first sentence embedding vector, performing vector merging and attention operation to obtain a first transformation matrix, performing first transformation processing on the first transformation matrix to obtain a second sentence embedding vector; after performing second transformation processing on the first sentence embedding vector and the second sentence embedding vector, performing SVD decomposition processing, inputting the obtained left singular matrix into a verb cause-effect relationship prediction model, and outputting a cause-effect relationship analysis result of the first verb and the second verb. The application can focus on learning relevant key information of the first verb and the second verb, reduce information redundancy, and improve the classification accuracy of the cause-effect relationship.

The invention relates to the technical field of intelligent document review, in particular to an intelligent document review method and system based on large model fusion causal reasoning, and the method comprises the steps: collecting documents, extracting formats and null lines, recognizing roles and actions, retrieving nouns after verbs, processing semantic repetition, outputting a table, checking causal connections, comparing action sequences, screening and joining, ranking verbs, and pulling through actions. And outputting causal fragments, checking consistency, separating objects, calibrating behaviors, outputting chain fracture, splitting adverbs, sorting relationships, and outputting conflict paths and review paths. According to the method and the device, by extracting the paragraph formats, roles and actions of the documents, the meticulousness of review is improved. And analyzing a causal conjunction and an action relationship, and identifying a causal relationship and a semantic conflict. The method comprises the steps of calibrating an action sequence, identifying missing parts, sorting multiple actions and role changes, optimizing an examination path, reducing manual intervention and improving efficiency.

The invention discloses an English sentence generation method based on a continuation theory and an intelligent system thereof. The method belongs to the cross technical field of artificial intelligence and computer-aided language learning. The technical problems that an existing language generation model lacks bilingual learning theory guidance, so that generated sentences are low in communication validity, and the deep semantic relation cannot be intelligently reflected are solved. According to the technical scheme, the method comprises the steps of receiving verbs and context parameters input by a user; calling a large language model, and activating a mapping network containing nine adversities in three dimensions of materialization, rationalization and dialectization; a multi-modal optimization mechanism is adopted and comprises a corpus-based adversity position adjustment sub-module, an error prediction-cognitive enhancement cycle sub-module, a semantic association enhancement sub-module and a weight distribution optimization sub-module, and collaborative screening and optimization are carried out on adversity combinations; and outputting the English sentence with the enhanced communication function. The method is mainly used for core engine construction of intelligent writing assistance, a self-adaptive language teaching system and cross-cultural communication software.

A system and method are provided for explaining ontological sub-graphs. The system and method include querying an ontology to determine a match between a query graph and a portion of an ontology graph. When there is a match, a subgraph representing the match is first translated into a simple summary using a simple language (e.g., triplets which include a subject and object corresponding to pairs of connected nodes in the subgraph and a verb / predicate representing a relation / edge in the subgraph that connect the pair nodes). This simple summary is then fed, as part of a prompt, to a large language model (LLM) that generates a human-readable summary based on the prompt.

A computer device that manages privilege delegation is disclosed. The computing device can insert a custom verb command into a plurality of verb commands corresponding to a file. The computing device can intercept a request to execute the custom verb command on the file by intercepting a request to create a context menu. The computer device can obtain information related to the request to execute the custom verb command by obtaining a file identifier of the file from the request to create the context menu. The computer device can determine whether to execute the custom verb command on the file according to second privileges different from the first privileges based on the information related to the request to execute the custom verb command. The computer device can cause the custom verb command to be executed on the file according to the second privileges.

Methods and systems provide for extracting next step sentences from a communication session. In one embodiment, the system receives a transcript including sentences produced during a communication session. The system classifies, using a trained classification model configured to identify next step sentences, one or more sentences in the transcript as next step sentences. Each next step sentence includes a first-person pronoun and a verb having a lemma corresponding to a predetermined set of actionable verbs. The system determines, based on the one or more sentences classified as next step sentences, one or more analytics comprising a number of next step sentences associated with a participant of the communication session. The system displays, via a user interface, the one or more analytics.

The application relates to the technical field of patent data management, in particular to a patent tree and a construction method thereof, which comprises the following steps: based on patent text data, the technical features in the patent text are analyzed, the technical features of each patent are classified and integrated, and a patent technical feature set is obtained. In the application, the accurate positioning of patent technical elements is realized through intelligent information extraction and semantic analysis, the deep semantic analysis of verbs and noun phrases is combined, the technical target and implementation mode of the patent are extracted in detail, and the functions are decomposed and integrated, so that the core content of the patent is presented in a clear and structured manner. Further, the technical features are segmented and integrated according to time nodes and application fields, and combined with time sequence fluctuation characteristic analysis, historical dependent features are identified, and the development state of the patent technology can be effectively predicted. Through feature matching and similarity calculation, the accurate grouping of the patent and the analysis of cross-field feature combination can be realized.

To reduce, when a term that needs a caution is detected from a text, a process load of such a detection, and to present, to a user, the detection result in an easy-to-understand form.SOLUTION: A morphological analysis unit 12 of a telop production assisting device 2 performs a morphological analysis on an input text, and a verb replacing unit 13 replaces an inflected form of a verb contained in the text having undergone the morphological analysis with an original form. Caution-for-use term detecting means 21 performs pattern matching between the text having undergone the verb replacement and caution-for-use term information 32, detects a caution-for-use term from the text having undergone the verb replacement, generates positional information on the caution-for-use term in the input text, and generates a detection result including the positional information. A communicating unit 11, by transmitting the detection result to a user terminal 3, causes the user terminal 3 to display, on a screen, the input text in a form in which a position of a term corresponding to the caution-for-use term is made apparent using the positional information.SELECTED DRAWING: Figure 2

The invention relates to the technical field of natural languages, and provides a semantic construction and execution method of a natural language, which comprises the following steps of: performing structured analysis on a natural language statement, identifying subject, action and object semantic units, and establishing logic relation mapping among the semantic units; calling a context adaptation strategy according to the application scene features, and adjusting the technical term expression precision of the semantic unit; selecting a matched action verb intensity level from a verb professional pedigree dictionary based on role attributes in a user portrait library; adding an action initiator prefix, an execution main body prefix or a state suffix through the relation explicit rule base; when the modification relation exists, a hierarchical assembly mode is adopted; when the main-object-object linear relation is adopted, a linear assembly mode is adopted; converting the assembled semantic units into recognizable executable statements, and executing the recognizable executable statements; when the execution fails, positioning an abnormal semantic unit; generating a correction statement according to the correction rule; and reassembling the corrected statement.

The invention relates to the technical field of text proofreading, in particular to an intelligent text proofreading method and system based on cognitive grammar, and the method comprises the following steps: collecting semantic expressions such as verb affairs, matching a standard graph expression for verification, executing word order reconstruction, limiting pronoun pointing, and automatically generating a proofreading result. According to the method, a semantic structure label system is established by collecting verbs, position words and grammatical relations, classification and integration of sentence pattern semantics are achieved, consistency verification is conducted by matching cognitive structure library standard graphic configuration, semantic mismatch structure structures can be recognized and recombined, potential fracture links can be positioned by tracking word semantic mutation points, and the recognition accuracy is improved. The word order is reconstructed and optimized to achieve ordered expression of the logic relation, the grammar defect position is defined through difference comparison between the reconstructed fragment and the correction item, the pronoun logic definition and sentence pattern expression accuracy are enhanced, the recognition and correction capacity of hidden semantic errors in diversified texts is improved, and the text proofreading effect is greatly improved.

The invention provides a personal information synonym library construction and information collection automatic understanding method in a Chinese privacy policy, which comprises the following steps: defining the type of personal information needing to be analyzed, appointing an initial synonym, constructing and training a Chinese text word vector model, and obtaining a Chinese text word vector model; constructing a personal information synonym library based on the similarity between each human information phrase and the word vector of the initial synonym; performing word segmentation, part-of-speech tagging and dependency syntactic analysis on a Chinese privacy policy to be analyzed by using a natural language processing technology, performing simplification based on verbs indicating personal information collection and a tagging result in the previous step, and extracting phrases describing personal information collection; and for each type of personal information in the personal information synonym library, generating a regular expression capable of covering the synonym expression of the personal information, matching the extracted phrases, and mapping the extracted phrases into uniform personal information representation. The method is helpful for analyzing the illegal collection behavior of the personal information in the application.

The invention discloses a self-adaptive action and object decoupling method and system for combined action recognition, and relates to the technical field of action recognition, and the method comprises the steps: obtaining video frames, carrying out the extraction of the video frames, obtaining video features, inputting the video features into a pre-established action encoder and an object encoder respectively, action semantic features and object semantic features are respectively output, and the semantic features comprise semantic-related motion features and semantic-independent motion features; performing feature optimization and alignment on the action semantic features and the object semantic features based on an adversarial independence constraint mechanism and a double-branch cross-modal alignment mechanism, and outputting the optimized action semantic features and object semantic features; and performing joint reasoning prediction on all candidate verb and object combinations based on the optimized action semantic features and object semantic features to obtain prediction results of the verb and the object, and fusing the prediction results of the verb and the object to obtain a final combination score.