38 results about "Natural language sentence" patented technology

Systems and methods for generating an enhanced error message are provided. An example method includes: receiving one or more raw error messages. The one or more raw error messages include one or more stack traces. The method further includes matching at least one raw error message of the one or more raw error messages to one or more error rules from a plurality of error rules. The one or more error rules include regular expression patterns. The method further includes parsing the at least one raw error message, based on the one or more matched error rules from the plurality of error rules; and generating one or more enhanced error messages, based on the at least one parsed raw error messages. The one or more enhanced error messages include one or more natural language sentences. The method further includes embedding the one or more enhanced error messages into a website.

Generating a semantic synopsis is provided. A dataset comprising structured data is received and semantic analysis of the dataset is performed to determine categories of information included in the datasets. A spatio-temporal analysis of the dataset is also performed to determine time periods and locations to which the dataset applies. A static synopsis of the dataset is then generated in natural language sentences that describes semantic information in the dataset and time periods and locations covered by the dataset. The synopsis can be customized according to a user role and search query data.

The application discloses a clinical medical examination knowledge base construction and application method, device, equipment and medium, relates to the computer technical field, and includes: converting the collected clinical medical data into corresponding dense vectors based on a preset large language model to obtain a target clinical medical examination knowledge base; performing a configuration operation of a dialogue retrieval mode on the target clinical medical examination knowledge base, and after the configuration is completed, performing vector conversion on a received natural language sentence to be replied based on the preset large language model to obtain a corresponding question vector; by calculating the cosine similarity between the question vector and each dense vector in the target clinical medical examination knowledge base, the most relevant target vector to the question vector is determined; and based on the preset large language model and the target vector, a corresponding target natural language reply sentence is generated to reply to the natural language sentence to be replied. In this way, the retrieval efficiency can be effectively improved, and the convenience of retrieval is further improved.

The embodiment of the invention discloses a statement conversion method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of extracting corresponding global constraint information according to semantics of a to-be-converted statement, and determining a first sub-statement corresponding to the global constraint information from the to-be-converted statement; determining global action range information corresponding to the first sub-statement according to the global constraint information, and determining a relationship conjunction between the global constraint information and the first sub-statement according to the first sub-statement; determining nested structure information according to the global action range information, the relational conjunction, the first sub-statement and the global constraint information; when the first sub-statement is not the atomic statement, performing statement decomposition on the first sub-statement to obtain updated nested structure information, and determining an intermediate language statement; and generating a conversion statement corresponding to the to-be-converted statement according to the intermediate language statement. According to the method, the quality of the formalized language statement obtained by converting the natural language statement is improved.

Embodiments of this application disclose a method and an apparatus for generating a natural language sentence describing a workflow, and a storage medium. The method includes: determining a subject placeholder based on a first selection operation performed on a language element library; determining, from a subject thesaurus associated with the subject placeholder a subject occupying the subject placeholder, where the subject corresponds to a resource; determining a predicate placeholder based on a second selection operation performed on the language element library; determining, from a predicate thesaurus associated with the subject, a predicate occupying the predicate placeholder, where the predicate corresponds to a service operation of the resource; and generating the natural language sentence based on the occupied subject placeholder and the occupied predicate placeholder, where the natural language sentence is adapted to be converted into a low-code representation describing a workflow, and the workflow represents that the resource performs the service operation. The workflow may be conveniently described by using the natural language sentence, which reduces implementation difficulty of the workflow, and enriches implementations of the workflow.

A system and method for query selection are provided. The method may include acquiring a natural language sentence, pre-processing to obtain a standard node sequence, constructing a node tree based on the relationship between an index node and other nodes, generating a data query command based on the node tree, querying data using the data query command, and filtering the results. The standard node sequence may include at least an index node and a condition node. The node tree may be used to characterize the index-condition combination. The system may include an acquisition unit, a pre-processing unit, a node tree construction unit, a translation unit, and a querying and filtering unit.

The invention discloses an electric power standard obtaining method and system based on a knowledge graph, and belongs to the technical field of electric power, and the method comprises the steps: S1, analyzing a natural language statement through a large language model to obtain a user structured intention, and obtaining a knowledge graph node corresponding to the user structured intention in the knowledge graph through knowledge graph embedding; s2, performing multi-modal traversal on the multi-modal index database by taking knowledge graph nodes as traversal anchor points and user characteristics as traversal constraints to obtain a candidate result set; and S3, obtaining weights of candidate result pairs in the candidate result set through a graph attention mechanism, and sorting the candidate result pairs based on the weights of the candidate result pairs so as to obtain an electric power standard. The technical problem that the suitability of the electric power standard is difficult to improve in the prior art is solved.

Provided is a method for identifying subtly biased texts within open corpora, which includes: a natural language sentence collecting step of collecting natural language sentences from text-based datasets including open conversation datasets and web corpus datasets; a bias candidate dataset obtaining step of comparing semantic vectors between words composing the natural language sentences by using a word-embedding model from the collected natural language sentences, thereby obtaining a bias candidate dataset including seed bias data expected to be socially and ethically biased and derived bias data expanded from the seed bias data; and a bias determining step of determining the bias candidate dataset as one of a surficial bias dataset, a subtle bias dataset, and a non-bias dataset by using multiple large-scale language models.

Provided are systems and methods and computer-implemented systems for generating synthetic data and optimizing performance of large language models, by integrating semi-automatic synthetic data generation, prompt optimization and continual learning into a unified system, without full fine-tuning of the model, to improve performance of the large language model. The cold start problem of the large language model is solved through a multi-stage process, including: generating a diversified parameter set and natural language sentences; constructing synthetic user queries using feature descriptions, parameter sentences and constraint instructions; bootstrapping with a small amount of samples with the help of a compiler, and iteratively evaluating instruction-example combinations to optimize the large language model module; and combining real-time data and synthetic data to complete continual validation and optimization.

According to some embodiments, systems and methods are provided including receiving a crash dump file; extracting a call stack from the received crash dump file, wherein the call stack includes one or more functions, the functions having ordered positions in the call stack; converting the extracted call stack to natural language sentences; converting the natural languages sentences to a first call stack matrix; receiving the first call stack matrix and a second call stack matrix at a crash model, wherein the crash model is a Siamese neural network model; determining a similarity score for the first call stack matrix and the second call stack matrix; and determining whether the first call stack matrix and the second call stack matrix represent duplicate crashes based on the similarity score. Numerous other aspects are provided.

The invention discloses a 3D scene semantic segmentation system and method for open vocabularies, and belongs to the technical field of vision and image processing, and the system comprises a point cloud input module which is used for inputting preprocessed point cloud data and forming a point cloud set; the scale point cloud feature extraction module is used for extracting geometric local features of the point cloud data in the point cloud set, then extracting context features of the geometric local features, and finally summarizing to obtain full-scale fusion features; the CLIP language embedding module is used for mapping a natural language statement into a semantic embedding vector through a CLIP text encoder and carrying out normalization processing on the semantic embedding vector; and a vision-language semantic alignment module. According to the method, the natural language tag input by the user can be matched with the 3D point cloud data, support for any semantic description is realized, and a model does not need to be trained again, so that flexible and semantic-rich three-dimensional space understanding is realized.

Disclosed in the embodiments of the present application are a conversion method and apparatus for a natural language sentence, and a storage medium. The method comprises: determining a first mapping relationship between a subject in a natural language sentence and a function block header of a function block, wherein the subject corresponds to a resource in a work cell; determining a second mapping relationship between a predicate in the natural language sentence and a function block name of the function block, wherein the predicate corresponds to a service operation of the resource; and on the basis of the first mapping relationship and the second mapping relationship, converting the natural language sentence into the function block, or converting the function block into the natural language sentence, wherein the function block is presented as a function block type diagram, the function block is adapted to describe a workflow, and the workflow represents the resource executing the service operation. A natural language sentence can be converted into a function block that is presented as a function block type diagram, thereby improving the efficiency of workflow execution.

Provided are a method and device for providing diagnostic information about spinal diseases using a natural language processing model related to spinal diseases. The method according to an embodiment includes receiving symptom information of a patient with spinal disease from the user terminal, wherein the symptom information of the patient with spinal disease includes a natural language sentence input by the user terminal; determining standardized information about the symptoms of the patient with spinal disease through a natural language processing model related to spinal diseases, based on the symptom information of the patient with spinal disease, wherein the spinal disease-related natural language processing model is a model trained to standardize symptom information of a patient with spinal disease based on chief complaints of a plurality of patients with spinal disease, wherein the standardized information about the symptoms of the patient with spinal disease includes standardized information on a body part where the symptom occurs, standardized information on a symptom type, and standardized information on symptom severity; determining diagnostic information about the patient with spinal disease based on the standardized information about the symptoms of the patient with spinal disease; and transmitting the diagnostic information about the patient with spinal disease to the user terminal.

A sentence generation device capable of generating a sentence in which information elements are included in a desired distribution is implemented. The sentence generation device that generates a sentence composed of a natural language sentence includes an information element distribution requirement acquisition unit that acquires an information element distribution requirement regarding a distribution of the information elements to be included in the sentence, and a sentence generation unit that generates the sentence so as to satisfy the information element distribution requirement using a language model. The generated sentences can also be utilized as training data for AI systems to facilitate automated decision making in diverse machine-learning scenarios.

To disclose a technique for improving user's convenience.SOLUTION: The computer program causes the computer to function as a first reception unit that receives an input of a first natural sentence, a first supply unit that supplies first natural sentence data representing the first natural sentence to a natural sentence processing unit when the input of the first natural sentence is received, and a first acquisition unit that acquires first object information based on the first natural sentence data from the natural sentence processing unit. The first object information is caused to function as: a first acquisition unit which includes first identification information for identifying the first object for each of M1 screens among the M screens; and a second display control unit which, when the first object information is acquired from a natural sentence processing unit, causes a display device to sequentially display M screens including the M1 screens according to the first object information, wherein the first object is emphatically displayed on each of the M1 screens.SELECTED DRAWING: Figure 6

The invention discloses a natural semantic recognition method based on a large model. The method comprises the following steps: constructing a context cue P1 according to a to-be-recognized natural language sentence and a predicate and a first template; inputting the context cue P1 into the large language model to obtain a semantic item; extracting a core semantic role of the semantic item and a description thereof from the semantic framework library; combining the core semantic role and the description thereof with all fixed non-core semantic roles and role descriptions corresponding to the semantic framework library to obtain all possible semantic roles and descriptions thereof; constructing a context prompt P2 according to all possible semantic roles and descriptions thereof and a second template; inputting the context cue P2 into a large language model to obtain a structured annotation result; and according to the natural language sentence to be recognized and the predicate, post-processing and correcting the structured labeling result to obtain a corrected recognition result. According to the natural semantic recognition method, large-scale manual data annotation is not needed, the development and deployment cost is remarkably reduced, and the development efficiency is improved.

To obtain highly accurate natural language sentences tailored to individual users for specific purposes. [Solution] The information processing system includes: an input information acquisition unit that acquires input information including natural language sentences input by a user to obtain natural language sentences based on first knowledge for a specific purpose; a model selection unit that selects a tuning model from one or more large-scale language models, which is a large-scale language model that has been fine-tuned using second knowledge different from the first knowledge for a specific purpose; a related knowledge acquisition unit that acquires related knowledge related to the input information from the first knowledge; and an output information generation unit that generates output information including related knowledge and natural language sentences corresponding to the input information using the tuning model.

This invention discloses a sign language recognition method based on hand keypoint fusion and a language model, comprising the following steps: A: acquiring preprocessed image frame sequence data containing hand movements; B: constructing a set of hand detection boxes and a set of hand keypoints based on a hand keypoint detection network; C: acquiring the corresponding hand keypoint feature vector in each frame; D: identifying the number of hands in the image to obtain sign language word markers; E: constructing a stable sign language word cache sequence to be output based on the acquired sign language word markers; F: dividing the stable sign language word cache sequence to be output based on the duration of no-hand movement in the image to obtain complete semantic units for output; G: generating natural language sentences based on a language model and combining the complete semantic units to complete the output of sign language sentences. This invention can achieve stable output of continuous sign language into natural language sentences and is suitable for deployment in embedded devices.

Correlation rules (or simply, rules) evaluate events received from networked components. The rules may fail to keep up with the events received and leave a network and / or the networked components vulnerable. Systems and methods are provided wherein an event is processed by rules, when known. When not known, and not associated with normal activity, the event is converted into a natural language sentence by a large language model (LLM), which is then provided to a sentence transformer to vectorize the sentence and associated events. A neural network then determines a degree of fit to a number of rules and selects the best fitting rule(s). If no rule is a sufficient match, a new / modified rule may be generated.

The invention discloses a language function rehabilitation corpus screening method based on semantic embedding and neural coding. The method comprises the following steps: firstly, performing semantic embedding processing on vocabularies in a vocabulary library by utilizing a pre-training semantic matching model to obtain semantic vectors of the vocabularies, and forming a plurality of semantic categories through clustering analysis; then semantic embedding processing is conducted on sentences in the large-scale corpus to obtain sentence semantic vectors, natural language sentences corresponding to semantic categories are screened out according to the sentence semantic vectors, and a candidate stimulation corpus is constructed; collecting functional magnetic resonance imaging data of the tested object in the process of presenting the candidate stimulation corpus, and constructing a coding model based on sentence semantic vectors and brain region activation intensity; and finally, inputting sentence semantic vectors in a candidate stimulation corpus into the coding model to obtain a predicted activation value of a language processing related brain region interest region, and screening out a target sentence according to the predicted activation value to form a rehabilitation training corpus set.

A graphical modeling computer system configured to: (i) generate a graphical model including each payment node of a plurality of payment nodes, each funding node of a plurality of funding nodes, and a transaction edge between the payment nodes and funding nodes representing transactions; (ii) retrieve personally identifiable information (PII) for each transaction; (iii) train the graphical model to include PII embeddings; (iv) convert the graphical model into natural language sentences describing each transaction and the associated PII embeddings; (v) generate, using the GAI tools, n-dimensional vector embeddings, including the associated PII, for the transactional edge; (vi) build and train a final graphical model including approved transactions and n-dimensional vector embeddings to apply the final graphical model to a subsequent transaction between a payment node and a funding node to determine if the subsequent transaction is a fraudulent transaction.

Techniques for generating content associated with a user input / system generated response are described. Natural language data associated with a user input may be generated. For each portion of the natural language data, ambiguous references to entities in the portion may be replaced with the corresponding entity. Entities included in the portion may be extracted, and image data representing the entity may be determined. Background image data associated with the entities and the portion may be determined, and attributes which modify the entities in the natural language sentence may be extracted. Spatial relationships between two or more of the entities may further be extracted. Image data representing the natural language data may be generated based on the background image data, the entities, the attributes, and the spatial relationships. Video data may be generated based on the image data, where the video data includes animations of the entities moving.

The application discloses a rule intelligent labeling generation method based on task semantic analysis, relates to the technical field of data labeling management, and specifically comprises the following steps: performing zero-sample rule cold start, comprehensively calculating confidence scores according to occurrence frequencies, semantic rationality scores and domain general prior knowledge, and selecting a seed rule set; acquiring the seed rule set, constructing a rule knowledge graph based on multi-dimensional semantic association relationships; acquiring a natural language sentence to be labeled, performing bidirectional refining on sentence semantics and rule semantics of the natural language sentence through a cross-attention mechanism, and calculating a matching score; on the rule knowledge graph, label information is propagated from the seed rule to a candidate rule through an attention mechanism; the propagated label and the matching label are compared, inconsistent rules are marked and preferentially submitted for verification; the rule knowledge graph is updated according to a verification result, and a final labeling rule set is iteratively generated.

The embodiment of the invention provides a triple extraction method and device based on GAN and template automatic construction, and the method comprises the steps: obtaining text data; on the basis of a BERT model, according to the text data, sentence feature vectors are obtained; obtaining a natural language statement according to the text data based on a GAN model; based on a pre-training language model, obtaining a real relation set according to the natural language statement; according to the text data, constructing a standardized relation semantic template; extracting an entity pair according to the sentence feature vector, the standardized relation semantic template and preset header data; and on the basis of a loss function, obtaining a triple according to the real relation set and the entity pair. According to the method, the overall performance and effect of entity relationship extraction can be remarkably improved.

To enable a user to easily find a desired commodity in a store.SOLUTION: A product search system (1) includes an input information acquisition unit (111) that acquires input information including a natural language sentence or an image input to a terminal by a user in a store, a product identifying unit (211) that identifies a product corresponding to the input information using a machine learning model machine-learned to output product identifying information for identifying any one of a plurality of products using the natural language sentence or the image as an input, and a product information presentation unit (213) that presents product information including a situation of the identified product to the user via the terminal with reference to store information including a situation of each product that can be sold in the store among the plurality of products.SELECTED DRAWING: Figure 2

A graphical modeling computer system configured to: (i) generate a graphical model including each payment node of a plurality of payment nodes, each funding node of a plurality of funding nodes, and a transaction edge between the payment nodes and funding nodes representing transactions; (ii) retrieve personally identifiable information (PII) for each transaction; (iii) train the graphical model to include PII embeddings; (iv) convert the graphical model into natural language sentences describing each transaction and the associated PII embeddings; (v) generate, using the GAI tools, n-dimensional vector embeddings, including the associated PII, for the transactional edge; (vi) build and train a final graphical model including approved transactions and n-dimensional vector embeddings to apply the final graphical model to a subsequent transaction between a payment node and a funding node to determine if the subsequent transaction is a fraudulent transaction.

Systems and techniques that facilitate semantics-guided domain-specific data augmentation for text-to-graph parsing are provided. In various embodiments, a system can access an annotated training dataset, wherein the annotated training dataset can comprise a set of abstract meaning representation graphs respectively corresponding to a set of natural language sentences. In various aspects, the system can generate an augmented version of the annotated training dataset, based on applying semantics-guided composition operations or semantics-guided substitution operations to the set of abstract meaning representation graphs. In various instances, a lexicon legend can comprise domain-specific graphs respectively representing discrete tokens unique to a domain of the annotated training dataset. In some cases, various of the domain-specific graphs can be composed or substituted onto or into various of the set of abstract meaning representation graphs, in response to semantic determinations, such as semantic-type-based determinations, argument-structure-based determinations, or incoming-semantic-relation-based determinations.

A system and method for query selection are provided. The method may include acquiring a natural language sentence, pre-processing to obtain a standard node sequence, constructing a node tree based on the relationship between an index node and other nodes, generating a data query command based on the node tree, querying data using the data query command, and filtering the results. The standard node sequence may include at least an index node and a condition node. The node tree may be used to characterize the index-condition combination. The system may include an acquisition unit, a pre-processing unit, a node tree construction unit, a translation unit, and a querying and filtering unit.

The application discloses a recommendation method and model training method and device, equipment and readable storage medium in the technical field of computer application, and the method comprises the steps of obtaining historical text information of an accessed text and candidate text information of a candidate text; inputting the historical text information and the candidate text information into a prompt template to obtain an answer word; selecting a mark from the answer word and converting the historical text information, the candidate text information and the mark into a natural language sentence; inputting the natural language sentence into a natural language processing model, predicting the mark by using an answer space, and obtaining a mark probability; and selecting a recommended text from a plurality of candidate texts based on the mark probability. The technical effect of the application is that the language information of a real world large-scale corpus can be fully utilized, and an individualized text intelligent recommendation task is converted into a mask prediction task based on multi-template prompt learning, so that content interested by a user is recommended.

Various disclosed embodiments are directed to deriving, via a language model, a summary of data by converting or encoding table data into one or more natural language sentences, which are then used as input to the language model for generating the summary. One or more embodiments are additionally or alternatively directed to deriving, via a language model, a response to a user question or command via a chat interface by providing the language model with the generated summary as input. In this way, for example, the language model can use the summary as a prompt or other target context for providing a response.