123 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.

Embodiments described herein provide an automated testing pipeline for providing a testing dataset for testing a trained neural network model trained using a first training dataset. A first testing dataset for the trained neural network including a first plurality of user queries is received. A dependency parser is used to filter the first plurality of user queries based on one or more action verbs. A pretrained language model is used to rank the remaining user queries based on respective relationships with queries in the first training dataset. Further, user queries that are classified as keyword matches with the queries in the first training dataset using a bag of words classifier are removed. A second testing dataset is generated using the ranked remaining user queries. Testing outputs are generated, by the trained neural network model, using the second testing dataset.

Disclosed herein are methods and systems for providing individualized responses and recommendations based on a shared knowledge language. A method of receiving a user query for a personalized response associated with a profile; interpreting the query by executing a machine-learning model; generating a data query corresponding to the user query by executing the machine-learning model, the data query configured for execution in a computer model comprising one or more nodes having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; receiving a first node of the computer model, wherein the first node is associated with the profile and generated based at least in part on an application accessed by the profile; presenting the personalized response, wherein the personalized response comprises an indication of the first node of the computer model.

The invention discloses an event detection and extraction method and system based on entity and trigger word fusion recognition. The method comprises the steps that trigger words in event text data are extracted; mapping the event text to a vector space based on a word embedding model, and constructing an event text classifier to perform similarity measurement on all words of which the part-of-speech is a verb and the trigger word in the event text; constructing an event detection model to obtain an event category in the event text data; identifying a key element entity of the event; constructing a dependency syntax tree to represent a syntactic dependency relationship of the sentences; obtaining the score of the dependency syntax tree; and inputting the preprocessed word vector and the part-of-speech vector representation obtained through supervised learning into a role tagging model for fusion to obtain a vector representation with part-of-speech features, and after training, carrying out conditional random field processing to obtain a final semantic role tag of the word. Accurate event detection based on entities is realized, and high-quality extraction of events is towed.

Systems and methods are presented herein for increasing user engagement with an interface by suggesting commands or queries for the user. A plurality of content items available for consumption are identified and metadata for each of the plurality of content items is retrieved. One or more candidate voice commands are generated based on a plurality of voice command templates based on a target verb and a subset of the metadata corresponding to the plurality of the content items available for consumption. A recall score is generated for each candidate voice command based at least in part on a detection of phonetic features that match between clauses of each candidate voice command. At least the candidate voice command with the highest recall score is selected and output using a suggestion system.

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.

The invention relates to a static and dynamic feature fusion video and text cross-modal retrieval method. The method comprises the following steps: constructing a noun, preposition and verb set for a text; converting nouns and prepositions into text static feature representation Tsf through a text encoder, and converting verbs into text dynamic feature representation Tdf; encoding a video through a video encoder to obtain video static feature representation Vsf; calculating and normalizing an inter-frame difference sequence of adjacent frames of the video, and encoding through a video encoder to obtain a video dynamic feature representation Vdf; calculating the static similarity between the Tsf and the Vsf, calculating the dynamic similarity between the Tdf and the Vdf, and carrying out weighted average on the Tsf and the Vsf to obtain a final similarity score; for a plurality of given texts and videos, k text-video pairs with the highest similarity scores are selected as retrieval results. According to the method and the system, the accuracy and the efficiency of video and text cross-modal retrieval can be improved, and videos related to text description can be effectively retrieved from a large amount of video data.

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 cross-modal livestock behavior retrieval method and device, and relates to the technical field of text-video retrieval. The method comprises the following steps: acquiring a livestock video and an input text input by a user, wherein the input text comprises a livestock behavior event; extracting a target sentence and nouns and verbs of the target sentence in the input text; based on a word set corresponding to the livestock behavior retrieval tag, determining a retrieval tag of a noun and a retrieval tag of a verb of the target sentence; searching video features matched with the retrieval tag of the noun, the retrieval tag of the verb and the sentence structure of the target sentence from the video-text multi-channel feature set; and based on the matched video features, retrieving livestock behavior events in the livestock video. Based on the scheme, the accuracy of retrieving the livestock behavior event from the livestock video based on the input text can be improved.

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.

Robot control systems, methods, control modules, and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters, environment details, and / or instruction sets may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. An NL response from the LLM may then be converted into a task plan. A task plan that successfully completes a first instance of a work objective may be parameterized and re-used to complete a second instance of the work objective. Parameterization of a task plan may include replacing one or more nouns / objects in the NL task plan with variables, while optionally preserving one or more verbs / actions in the NL task plan.

A computer-implemented method for automatically generating a REST API specification based on a plain-text user story comprising: receiving the plain-text user story comprising a number of plain-text sentences that constitute a requirements definition for an API-driven control application to be developed; identifying a number of nouns and a number of verbs in the acquired plain-text user story; matching the identified number of verbs to one of a plurality of HTTP methods; matching the number of identified nouns to a number of HTTP resources; building a REST path using the matched HTTP method and the number of matched HTTP resources; and generating and outputting a REST API specification comprising at least the built REST path. Stability of a system to which the control application is deployed is increased. Also proposed is a corresponding computing device.

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.