73 results about "Syntactic structure" patented technology

Certain aspects of the present disclosure describe a method of information retrieval. In certain aspects, the method includes identifying a set of relevant nodes of a document graph embedding semantic units associated with the document based on a document search query. The method further includes reconstructing a structural context for each relevant node in the set of relevant nodes. The method further includes processing the set of relevant nodes and the structural context of each relevant node with a large language model to generate a contextual response to the document search query.

The invention discloses an aspect-level sentiment analysis optimization method based on multivariate external knowledge fusion, which relates to the technical field of sentiment analysis optimization, and comprises the following steps: constructing a multivariate external knowledge source comprising a Chinese sentiment dictionary, a domain knowledge graph and a user comment prior mode library; the sentiment module is used for providing vocabulary-level sentiment polarity, entity attribute relations and high-frequency evaluation semantic modes; semantic coding is performed on the input text and the specified aspect words to generate context semantic representation, and global semantic features and local position features are extracted in combination with aspect word position information; based on a semantic coding result, converting the multivariate external knowledge sources into structured knowledge representations, and dynamically adjusting contribution weights of various types of knowledge through a gating fusion mechanism to generate fused knowledge representations; performing dependency syntactic analysis on the input text, constructing an original syntactic structure, and calculating the correlation strength of each grammatical component and aspect words in combination with context semantic representation; and pruning the original syntactic structure according to the correlation intensity.

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a large-model complex instruction-oriented fine-grained intention recognition method, which comprises the following steps of: constructing a candidate analysis forest of an input instruction, and executing entity alignment of each node and a domain knowledge graph; performing semantic compatibility verification on the candidate dependency trees by utilizing entity relationships in the atlas, and screening an optimal analytic tree in combination with syntactic probability and knowledge consistency scores; if the analytic tree meeting the threshold value does not exist, a semantic conflict edge is positioned, and candidate mounting points are searched by using a map neighborhood relation to reconstruct a dependency structure; and finally, generating standardized data containing a structured analytic path based on the optimal analytic tree, and performing fine adjustment on the large model. According to the method, logic constraint and dynamic repair are carried out on the syntactic structure by introducing the knowledge graph, analysis errors caused by multiple modification or structural ambiguity in a complex instruction are effectively solved, and the accuracy and robustness of intention recognition of a large model in the vertical field are improved.

The invention discloses an output content detection method based on a semantic analysis detection model, and the method specifically comprises the following steps: S1, collecting multi-modal data, and carrying out the unified coding of the data, and generating a structured semantic vector; s2, analyzing the structured semantic vector, extracting a subject-predicate-object triple, and generating a dynamic semantic vector matrix; s3, constructing a two-channel semantic detection model, and outputting syntactic structure scores and semantic feature scores according to data in the dynamic semantic vector matrix; s4, calculating a comprehensive risk value according to the syntactic structure score and the semantic feature score, and performing dynamic joint decision making; s5, generating a three-dimensional detection report according to a decision result; and S6, dynamically adjusting and optimizing the model according to the content of the detection report. According to the method, multi-dimensional quantification of complex semantics is achieved, misjudgment and missed judgment of traditional single threshold judgment are reduced, a user is assisted in efficiently processing problems, and the long-term application requirement in a complex scene is met.

The techniques discussed herein enhance the operation of content generation and analysis systems. Namely, textual content applications such as technical documentation, creative writing, and content moderation. This is accomplished through generating a graphical representation of a body of text (e.g., a document). The graphical representation can comprise a plurality of nodes representing the words of the document and a plurality of lines that join the nodes representing a level of association between individual words. As such, the graphical representation can capture the semantic and syntactical structure of the associated document while omitting the original textual content. The graphical representation can be subsequently evaluated for complexity based on the density of nodes and lines. Accordingly, the disclosed system can assign a score to a document based on the evaluation of the graphical representation. In addition, various documents can be ranked based on such scores.

The embodiment of the invention provides an ancient book proofreading method and device and a storage medium. In the method, each ancient book page of an ancient book file is divided into a plurality of ancient book segments, a text segment corresponding to each ancient book segment is generated, and a first ancient book character of each ancient book segment corresponds to a second ancient book character in the corresponding text segment; according to the first ancient book character in any ancient book segment, performing letter proofreading on the second ancient book character in the text segment corresponding to any ancient book segment to obtain first proofreading information; according to the syntactic structure in any ancient book page and the first proofreading information in the text page, performing word and sentence proofreading on the text page to obtain second proofreading information; and according to the first and / or second proofreading information in the text file corresponding to the ancient book file, carrying out grammar proofreading on text contents with impaired meanings in the text file to obtain a corrected text file. In this way, the ancient book recognition text can be corrected from multiple dimensions of characters, words, sentences and semantics, and the accuracy of the recognition text is improved.

The application discloses a machine translation software defect detection method based on combined semantics, comprising the following steps: S1, obtaining different Chinese translation sentences of the same English source sentence from different translation software; S2, using a word alignment model to correspond English words in the source sentence with segmented Chinese words in the Chinese translation sentences; S3, using a sentence compression model and a syntactic structure analysis method to obtain a main part and each additional part of the English source sentence respectively and form a sub-sentence set; S4, aligning each part of the English source sentence obtained in the step S3 with the corresponding translation part to obtain aligned translation of the main part and the additional part of the source sentence; and S5, detecting errors, including semantic similarity calculation and synonym checking.

The invention relates to the technical field of natural language processing and mapping knowledge domain, and particularly discloses a mapping knowledge domain relation extraction method fusing a syntactic structure and a domain rule. The method aims at solving the problems of semantic understanding deviation and insufficient domain knowledge utilization in vertical domain relation extraction. The method comprises the steps that dependency syntactic analysis is conducted on a text, a syntactic dependency adjacency matrix is constructed, and meanwhile a rule adjacency matrix is generated based on domain rule base matching; carrying out weighted fusion and filtering on the two types of matrixes to obtain an enhanced adjacent matrix; inputting the matrix and a text vector into a graph convolutional network fused with a dependency type attention mechanism, and learning to obtain a node enhancement representation; and finally constructing an entity pair feature vector to finish relationship classification. Through explicit fusion of interpretable syntactic rules and domain priori, the accuracy and robustness of relation extraction in professional fields such as a power distribution network are improved. Experiments show that the model relation extraction F1 value reaches 86.07% and is improved by 1.31% compared with a baseline model with the optimal performance.

The invention relates to a low-resource event argument extraction method based on a language consistency example, and belongs to the technical field of event argument extraction. According to the method, by introducing an example selection mechanism based on language consistency, the accuracy of event argument extraction under the low-resource condition is effectively improved. Compared with an existing method for conducting example matching only according to sentence-level semantic similarity, the method has the advantages that the granularity of semantic modeling is refined, the consistency of a syntactic structure is further combined, it is ensured that the selected example is highly consistent with a target event in the aspects of event trigger words, argument distribution, syntactic dependency and the like, and the accuracy of example matching is improved. And the method has relatively high precision when low-resource event argument extraction is carried out.

The invention, which relates to the technical field of English parsing, discloses an intelligent parsing system for a long and difficult English sentence structure in combination with context features, comprising a context feature hierarchical extraction module, a dynamic coupling association module, an adaptive weight adjustment module and a syntactic structure parsing module. According to the method, a three-layer context feature layered extraction model of a syntactic structure layer, a chapter semantic layer and a sentence pattern function layer is constructed, the multi-dimensional context features of English long and difficult sentences are dynamically coupled and associated, and the feature analysis weight of each layer is optimized in real time according to the context feature distribution through an adaptive weight adjustment module; synchronous linkage of syntactic structure analysis and context semantics is achieved, the problems that in the prior art, due to the fact that multi-dimensional context association is ignored, nested subordinate sentence splitting errors, logic confusion of master and slave sentences, core component positioning deviation and the like are caused are solved, and the accuracy of English long-difficult sentence structure analysis is improved; and a technical support is provided for application scenes depending on long and difficult sentence analysis, such as machine translation, academic literature research and reading, language teaching and the like.

A system is provided for determining a natural language output, responsive to a user input, using different speech personality profiles. The system may determine to user a particular language generation profile based at least in part on data relating to the user input and data corresponding to the response to the user input. The language generation profile may include different attributes that are used to determine the natural language output, such as, prosody, replacement words, injected words, sentence structure, etc.

A computer-implemented process is programmed to programmatically receive, using a first computer system, electronic digital data representing input time-correlated speech data and video data, determine a first text sequence corresponding to the input time-correlated speech data, the first text sequence comprising unstructured natural language text, determining syntactic structure data associated with the first text sequence, inputting the time-correlated video data and the syntactic structure data associated with the first text sequence into one or more machine learning models, the machine learning models producing an output of one or more scores for at least a portion of the time-correlated video data and first text sequence, transforming the output of one or more scores to yield and output set of summary points and suggestions, and transmitting a graphical element of the output set of summary points and suggestions for display.

This invention relates to the field of natural language processing technology, specifically to a triage method and system for neurological diseases, comprising the following steps: segmenting the syntactic structure of the patient's chief complaint text sequence and identifying nodes; calculating and quantifying the nesting degree of the patient's linguistic organization logic; and obtaining a syntactic potential depth value. This invention can filter out noise interference from complex clinical expressions and extract neurological syndrome features by comparing them with a preset internal capsule lesion pattern. By jointly analyzing feature tensors pointing to limb paralysis with syntactic potential depth values ​​below the normal threshold, dual cross-validation of somatic motor disorders and central cognitive impairment is achieved. Furthermore, by comparing with feature thresholds of peripheral nerve or spinal cord lesions, the specificity of identifying central acute and severe illnesses such as ischemic stroke is improved, optimizing the accuracy of emergency triage.

The invention relates to a neural machine translation selective knowledge distillation method based on dependency constraint self-attention, and belongs to the technical field of machine translation. An existing knowledge distillation method has the problems that only vocabulary-level probability distribution is transmitted, syntactic structure constraints are ignored, and the capacity of a student model is reduced, so that the complex syntactic modeling capacity is insufficient. Therefore, according to the method, a syntactic matrix converted through a source language dependency syntactic tree is provided, linear combination is adopted to dynamically correct self-attention weight distribution of an encoder, and explicit syntactic constraints are synchronously injected into a teacher-student model. Through a selective distillation strategy of syntax perception, deep knowledge effective for training in a teacher model is screened, a cross-language syntax corresponding relation is obtained in combination with structure alignment distillation, and model compression and translation performance enhancement is realized through attention optimization and a selective knowledge transmission mechanism guided by a dependency syntax.

The invention discloses an ancient language machine translation method for syntactic perception and knowledge enhancement, and belongs to the technical field of natural language processing. Aiming at the difficulties of complex sentence patterns and scarcity of historical knowledge of ancient Chinese, three innovation modules are designed: firstly, a dynamic dependency syntactic analysis module explicitly models an ancient Chinese syntactic structure by utilizing probability analysis and a graph network; secondly, a retrieval enhancement generation module accurately extracts related knowledge fragments from the high-quality ancient language corpus, and semantic comprehension is enhanced; and finally, the stream grammar constraint decoder fuses the source language syntax and the target language generation process through a double-stream mechanism and a stream mechanism, and loyalty and smooth modern text output is realized. The three modules are deeply fused, so that the accuracy and interpretability of ancient language translation are effectively improved, and a reliable technical path is provided for ancient book digitization.

The present disclosure relates to systems and methods for generating contextually, grammatically, and conversationally correct answers to input questions. Embodiments provide for linguistic and syntactic structure analysis of a submitted question in order to determine whether the submitted question may be answered by at least one headnote. The question is then further analyzed to determine more details about the intent and context of the question. A federated search process, based on the linguistic and syntactic structure analysis, and the additional analysis of the question is used to identify candidate question-answer pairs from a corpus of previously created headnotes. Machine learning models are used to analyze the candidate question-answer pairs, additional rules are applied to rank the candidate answers, and dynamic thresholds are applied to identify the best potential answers to provide to a user as a response to the submitted question.

The invention discloses an agrometeorological disaster relation extraction method, and belongs to the technical field of agrometeorological disaster relation extraction, and the method comprises the steps: firstly carrying out the cleaning and entity and relation marking of multi-source heterogeneous agrometeorological text data, and completing the structural preprocessing; then, through a parallel processing framework, utilizing a BERT pre-training model to extract deep semantic features of the text, and combining dependency syntactic analysis to construct a syntactic structure chart; further, a self-attention graph convolutional network is adopted to carry out dynamic pruning and feature learning on the syntactic structure, and a residual shrinkage network is introduced to carry out intelligent noise reduction processing on features; and finally, performing high-precision relationship classification by fusing semantic and syntactic features, and realizing automatic extraction of key information such as disaster time, place, loss degree and the like. According to the method, the relation between the entities can be efficiently and accurately recognized in the text in the field of agrometeorological disasters, and the accuracy, robustness and automation level of agrometeorological disaster relation extraction are remarkably improved.

This invention discloses a method, system, device, storage medium, and product for searching fault solutions. It involves using a search engine to perform preliminary matching of fault descriptions to obtain a set of candidate fault solutions; extracting semantic vectors from both the fault descriptions and candidate fault solutions; constructing a syntactic dependency tree for the fault descriptions and candidate fault solutions to obtain a graph structure; inputting the graph structure and the corresponding semantic vectors into a graph convolutional neural network to obtain syntactic feature vectors for the fault descriptions and the candidate fault solutions; bidirectionally fusing and concatenating the syntactic feature vectors to obtain a fused feature for each candidate fault solution; and ranking the candidate fault solutions based on the fused features to obtain an ordered fault solution matching the fault description. This invention enables deep capture of semantic and syntactic structures to improve search relevance and provide more accurate results for fault solution searching.

This invention discloses an ATL-based method for converting SLIM models to XSAP models, comprising: designing a SLIM metamodel and an XSAP metamodel according to predefined model conversion rules, and obtaining the abstract syntactic structures of the SLIM metamodel and the XSAP metamodel; designing a model conversion algorithm based on ATL, utilizing the metamodel and abstract syntactic structures to convert the SLIM model to an XSAP model, obtaining a converted XML-formatted XSAP model; and designing a verification algorithm to verify the XSAP model, obtaining a complete SMV-formatted XSAP model. This invention can directly convert safety-critical systems from SLIM models to XSAP models and perform verification analysis on the converted XSAP models. The conversion algorithm and model verification algorithm proposed in this invention have universality.

This invention discloses an AIGC text detection method and system based on multi-dimensional feature fusion, belonging to the field of natural language processing. It addresses the performance bottlenecks and interpretability issues of existing technologies in text rewriting / polishing and multi-model hybrid content detection. Closed-loop detection is achieved through S1 multi-dimensional feature extraction (four orthogonal dimensions: statistical linguistics, semantics and fluency, syntactic structure, and information theory), S2 attention-weighted feature fusion (Z-score normalization + dynamic weighting by an attention network), S3 adaptive threshold determination and confidence evaluation (dynamically generating thresholds based on text metadata), and S4 interpretability report generation. The system includes feature extraction, fusion, discrimination, and report generation modules, as well as offline construction and online inference units. It balances detection accuracy, anti-circumvention robustness, cross-scenario generalization ability, and result interpretability, with low inference costs and easy scalability.

This invention discloses a Chinese sentence classification method that integrates dependency syntax and global co-occurrence information. First, text data is collected for preprocessing and syntactic analysis. Then, a pre-trained language model aggregates sub-word-level features into word-level contextual semantic features. Based on the syntactic analysis results, a dependency graph and a word-part-of-speech co-occurrence graph are constructed. Word-level features are used as node features and input into a graph attention network and a graph convolutional network respectively to complete dual-graph modeling, obtaining syntactic enhancement features and global co-occurrence enhancement features. Subsequently, an adaptive weighted fusion of the two types of enhancement features is performed through a gating mechanism. The fused features are concatenated with the original word-level features and input into a Transformer block to complete global self-attention interaction. Finally, the interacted features are pooled and linearly mapped, and the label with the highest probability is used to achieve Chinese sentence classification. This invention solves the problem that existing technologies mainly rely on pre-trained language model context representations, making it difficult to fully model syntactic structural information and global statistical knowledge.

Here is message compression using schema inference for condensing semantic content by removal of syntactic structure in multiple kinds of content. Topological structures of different trees are generalized to generate a merged tree. Because compression discards redundant content and often only semantic content is retained, the signal-to-noise ratio is increased, which increases accuracy of downstream semantic analytics such as machine learning. Compression based on the merged tree removes redundant information from new messages that, without obscuring semantic content, decreases the data volume for downstream analytics or archiving. This compression extracts semantic values that can be assembled into a sequence of lexical tokens that is suitable for natural language processing (NLP), and the sequence of lexical tokens does not contain tokens that represent syntax or structure. Thus, compression provides fewer tokens to be processed by a downstream language model, which is suitable for efficient processing of a live data stream.

The invention discloses a hybrid reasoning BIM (Building Information Modeling) design achievement auditing method for complex standard specification provisions. The method comprises the following steps: S1, defining a grammar structure and an entity class in a standard specification ontology; s2, constructing a standard specification knowledge graph based on the natural language large model; s3, constructing a mapping table; s4, constructing a design result file and text description information; s5, generating an executable rule statement; and S6, performing hybrid reasoning based on a rule reasoning engine and a natural language large model cue word technology. The method comprises the following steps: disassembling a standard text by using a natural language large model cue word technology, and constructing a knowledge graph; the natural language standard specification text is converted into an executable rule, and compliance auditing of the BIM design result is supported; and a rule inference engine and a large language model are combined to perform hybrid inference, so that the BIM model auditing precision is improved.

The present disclosure relates to systems and methods for generating contextually, grammatically, and conversationally correct answers to input questions. Embodiments provide for linguistic and syntactic structure analysis of a submitted question in order to determine whether the submitted question may be answered by at least one headnote. The question is then further analyzed to determine more details about the intent and context of the question. A federated search process, based on the linguistic and syntactic structure analysis, and the additional analysis of the question is used to identify candidate question-answer pairs from a corpus of previously created headnotes. Machine learning models are used to analyze the candidate question-answer pairs, additional rules are applied to rank the candidate answers, and dynamic thresholds are applied to identify the best potential answers to provide to a user as a response to the submitted question.

The invention belongs to the technical field of natural language processing, and particularly discloses an aspect-level sentiment analysis method based on syntax and semantic fusion. Capturing context sequence features by adopting a bidirectional long-short-term memory network, and extracting deep syntactic structure features through two-channel syntactic features of a component syntactic tree and a dependency syntactic tree; semantic prior information of a SenticNet emotion knowledge base is integrated, potential semantic association is mined in combination with multi-head self-attention, and feature focusing of a target aspect word is enhanced by adopting a dynamic mask technology; self-adaptive weighted aggregation of syntactic-semantic information is realized through a feature fusion layer, and accurate emotional polarity prediction is realized by using an MLP and Softmax classifier after multi-granularity pooling processing and feature splicing. According to the method, syntactic structure constraints, semantic association reasoning and external emotion knowledge are integrated, context sensitive modeling of the words in the target aspect is achieved through self-attention, and the judgment precision of the emotion tendency in the specific aspect in the complex language environment is remarkably improved.

Disclosed subject matter relates to verification and debugging tool and method for providing sensitive regions around and in Register Transfer Level (RTL) code objects. The verification and debugging tool includes input interface configured to receive RTL code in Hardware Description Language (HDL). Further, verification and debugging tool includes RTL parser configured to parse RTL code, and generate abstract syntax tree representing syntactic structure of RTL code. Thereafter, verification and debugging tool includes graph converter configured to convert abstract syntax tree into RTL directed graph. Furthermore, verification and debugging tool includes visual display configured to display code blocks, each exhibiting sensitive regions. Finally, verification and debugging tool includes objects extractor configured to detect user actions related to sensitive regions. The sensitive regions are provided around and in each of RTL code objects to facilitate extraction and display of relevant information regarding dependencies and operations of RTL code upon user interactions.

The invention belongs to the technical field of natural language processing, particularly relates to a text mining-based air-taking service case semantic extraction and network construction method, and aims to solve the problem of how to intelligently extract effective information of air-taking service cases. The method comprises the following steps: acquiring an air-ride service case text, and preprocessing the text to form case text data; analyzing a syntactic structure relationship among words in the case text data by utilizing a HanLP algorithm, and screening out a semantic core structure relationship; taking part-of-speech and word frequency information into consideration, improving a feature extraction algorithm, and extracting keyword items; based on adjacent positions of words in the feature data, analyzing to obtain directed association features among the words, and obtaining directed association phrases; constructing a co-occurrence network based on the directed association features and the directed association phrases, and performing modular clustering analysis; and key node links and high-influence node groups are extracted from the co-occurrence network, so that efficient and accurate extraction of service case work key points and core elements is realized.

This invention provides a blockchain-based method and system for software copyright trading and protection, specifically relating to the field of blockchain application technology. The system allows software vendors to submit different versions of software information and stores this information in the distributed file system IPFS. By indexing the hash values ​​of this information in the blockchain, the immutable storage of copyright information is achieved. Furthermore, the system uses an abstract syntax tree to extract the syntactic structure of the software code, further enhancing copyright protection. The digital signature generated from the copyright information is recorded along with the copyright information in the blockchain node, ensuring the traceability of the copyright information. When a copyright transaction occurs, the system generates a digital signature containing the copyright information and writes it along with the transaction information into the blockchain node, recording every copyright transaction and achieving transparent traceability of copyright history. In addition, the system also provides a method for comparing suspected software plagiarism, allowing software authors to upload suspected infringing software and identify and warn of potential plagiarism by comparing the abstract syntax tree structure stored on the blockchain. The method and system of this invention not only improve the efficiency and security of software copyright protection but also provide a reliable and transparent platform for software copyright trading, helping to promote the rational circulation and utilization of copyright.

Authentication device with sentiment analysis for secure access control, comprising a modular computer-based structure with coordinated subsystems for data acquisition, affective analysis, behavioral modeling, and access decision computation, wherein the device acquires multimodal input data from a user via an input module that captures text information, speech signals, and facial images, wherein each data stream is encrypted, time-stamped, and synchronized for analysis, and wherein a sentiment analysis unit processes the acquired data by applying speech processing to interpret lexical and syntactic structures, acoustic signal processing to evaluate tonal and prosodic features, and image processing to detect facial expressions and micro-movements in order to generate a sentiment vector representing the user's affective state.