31 results about "Syntactic tree" patented technology

The invention belongs to the technical field of operation monitoring of electric energy meters, and discloses an operation data monitoring and analyzing method and system based on an electric red-keyed electric energy meter, and the method comprises the steps: deploying the electric red-keyed electric energy meter in a low-voltage transformer area, collecting multi-dimensional electric energy operation parameters in real time, carrying out the collection and time axis reconstruction of the multi-dimensional electric energy operation parameters according to nodes, and carrying out the monitoring and analysis of the operation data of the electric red-keyed electric energy meter. Generating a load behavior time sequence, identifying a load behavior event by using a feature detection algorithm, and representing the load behavior event as a behavior lexical element; analyzing a hierarchical dependency relationship between a trunk mode and a modification structure of a load behavior event by constructing a syntactic tree structure of a load behavior, identifying a structural abnormal load mode, and generating an abnormal disturbance subgraph; fusing the abnormal disturbance sub-graph with a preset power supply topological structure, constructing a path graph with a body, executing a graph transformation operation group in the path construction process, and generating a multi-disturbance path sub-graph; and the accuracy, timeliness and reliability of load anomaly detection of the power distribution area are improved.

The invention belongs to the technical field of computer application, and discloses a video content understanding method and device based on structured grammar information, electronic equipment and a storage medium, and the method comprises the steps: inputting a training sample into a target model for content understanding processing, obtaining a prediction text, and constructing a syntactic tree corresponding to the prediction text; calculating a syntactic tree editing distance between the syntactic tree and the syntactic tree of the reference text; calculating language structure loss by using the syntactic tree editing distance, and updating model parameters of the target model by using the language structure loss; under the condition that the model parameters of the target model are trained, obtaining a target video; and inputting the target video into the target model for processing to obtain a content text of the target video. In the application, the target model is trained based on the language structure loss calculated based on the syntax tree, and the target video can be understood, so that the content text with accurate grammar and reasonable and natural sentence structure is obtained.

The invention relates to the technical field of natural language processing, in particular to a risk review method and system for contract terms, and the method comprises the following steps: obtaining a to-be-reviewed contract text, deconstructing sentences of each contract term, and obtaining a term analysis feature set; according to the method, contract terms are disassembled into basic semantic units sentence by sentence, feature details of syntactic tree depth, anaphora distance, logic nested layers and terminology density are extracted, four types of index combinations in a feature set are analyzed through terms, measurement of single-term analysis complexity scores is achieved, score aggregation of the complexity is further analyzed through terms, and the complexity of the contract is analyzed. According to the method, a specific index capable of quantifying the overall understandability risk of a contract is formed, topic clustering and semantic weight distribution are executed on a to-be-examined contract and a standard contract text library at the same time, the actual emphasis condition of each topic is determined, and the topic with large semantic deviation is positioned as a missing risk for targeted marking.

The application provides a specific target sentiment analysis method based on multi-level semantic coding, which comprises the following steps: S1, obtaining a text to be analyzed, and performing word segmentation on the text to be analyzed; S2, using a syntax analysis tool to analyze the word segmentation result, generating a dependency syntax tree, and constructing a syntax cover matrix according to the dependency syntax tree; S3, converting the word segmentation result of the text to be analyzed into a word vector two-dimensional matrix, using a transformer model to code the word vector two-dimensional matrix, and obtaining the shallow semantic features of the text to be analyzed; S4, inputting the syntax cover matrix and the shallow semantic features into a syntax attention network to obtain deep semantic features; and S5, calculating the text representation vector of a specific target according to the deep semantic features, and finally obtaining the sentiment classification of the specific target. The method can be applied to the specific target sentiment classification task, and the scalability of the syntax attention component also provides a new idea for other tasks in the natural language processing field.

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 relates to a user set speech viewpoint analysis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring comment data about a target object of each user in a user set and character attribute information of each user; performing part-of-speech tagging on the comment data to obtain a plurality of fragments, constructing a dependency syntax tree according to the dependency relationship among the fragments, and analyzing the dependency syntax tree to obtain viewpoint features in the comment data; performing weighted summation according to the viewpoint polarity score and the polarity weight value corresponding to each viewpoint feature in the feature score table to obtain a personal viewpoint polarity score, and determining a personal viewpoint according to the personal viewpoint polarity score; and calculating an attribute weight value according to the character attribute information, calculating a viewpoint attribute score according to the personal viewpoint polarity score and the attribute weight value of each user, and determining a user set viewpoint according to the viewpoint attribute score. By adopting the method, the reliability and efficiency of user set viewpoint analysis can be improved, and intelligent speech management support is provided for organizations such as enterprises.

The application discloses an aspect-level sentiment analysis method, system and device based on emotional knowledge and a medium, and can be widely applied in the technical field of sentiment analysis. The application obtains the dependency syntax tree of an input sentence, constructs the dependency graph structure of the sentence according to the dependency syntax tree, and then fuses the emotional knowledge and the position information into the dependency graph structure to obtain the adjacency matrix expressing the correlation between the nodes in the sentence. The global sharing of the emotional knowledge node parameters realizes the storage, updating and sharing of the emotional knowledge, thereby improving the fusion degree of the emotional knowledge. Then, the aspect-level sentiment analysis model is trained according to the sentences in the review corpus and the adjacency matrix, thereby reducing the cognitive bias of the model and improving the prediction performance of the model.

This application discloses a text readability analysis method, system, and electronic device based on NLP processing. The method extracts multiple character entities; performs word segmentation on the preprocessed text to be analyzed to obtain a word sequence and constructs a dependency syntax tree; based on the multiple character entities, the preprocessed text to be analyzed, and the dependency syntax tree, it extracts features such as the number of characters, the entropy of character personality diversity, the conflict density, the dialogue ratio, the relationship density, and the relationship complexity; it processes and fuses these features to obtain a fused feature vector; it inputs the fused feature vector into an encoder to obtain a preliminary score vector; and it determines the readability score of the text to be analyzed based on the preliminary score vector. This application can improve the accuracy of text readability analysis.

The application discloses a text language automatic detection method and system based on syllable and affix features, relates to the field of natural language processing, and comprises the following steps: text preprocessing and basic language unit extraction are performed on a text to be detected to obtain a text syllable sequence and an affix list; based on the affix list, a multidimensional pragmatic scene distribution vector in the context of the text syllable sequence is extracted, and core connectivity in a pre-constructed morphological paradigm knowledge graph is acquired; the function load value of the affix is matched from an affix function load database, the affix function load value is normalized, and a weighted affix feature vector is generated; syntax dependency relationship analysis and preliminary structure construction are performed on the text to be detected, and a dependency syntax tree of the text to be detected is generated. The application improves the detection accuracy and result interpretability of highly similar languages and mixed code texts by explicitly modeling and quantitatively evaluating deep morphological-syntactic rule features of languages.

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.

A grammatical error correction method incorporating component syntactic information belongs to the field of artificial intelligence technology. It includes: extracting component syntactic information from a given statement; using the erroneous statement, its corresponding correct statement, and the component syntactic information as input data; and constructing a multi-task grammatical error correction model that integrates component syntactic information to correct the erroneous statement. This method first extracts component syntactic information from the statement using grammatical analysis, serializes the statement's component syntactic tree to obtain a component syntactic sequence, and constructs triplet pairs as input data. Second, an adapter module is introduced to construct a multi-head attention mechanism model based on multi-task learning, learning the potential relationships between erroneous and correct statements, and between erroneous statements and component syntactic sequences. Finally, through pre-training and lightweight fine-tuning of the adapter module, the features of the erroneous statement, the correct statement, and the component syntactic sequence are fused to complete the grammatical error correction.

The present application relates to the technical field of natural language processing, and provides a translation method and device, electronic equipment and a storage medium, wherein the method comprises: determining to-be-translated data and a scene label corresponding to the to-be-translated data; performing dependency syntax analysis on the to-be-translated data to obtain syntax tree data representing the grammatical dependency relationship between words in the to-be-translated data; and translating the to-be-translated data based on the scene label and the syntax tree data to obtain target translation data corresponding to the to-be-translated data, which not only effectively solves the problems of part-of-speech misjudgment of polysemous words and structural confusion in translation from an agglutinative language to a fusional language, but also takes into account the grammatical accuracy of the language structure and the business professionalism of the specific field expression, greatly improving the translation accuracy and the final text quality of machine translation in the vertical field and complex conversational scenarios.

The embodiment of the application belongs to the field of artificial intelligence, and relates to a text abstract generation method based on semantic blocks, comprising the following steps: obtaining sample data, performing component syntax analysis on the sample data to obtain a syntax tree, determining semantic blocks according to the syntax tree, inputting the semantic blocks into a pre-constructed initial text abstract model, encoding the semantic blocks to obtain semantic block codes, performing weighted calculation on the semantic block codes to obtain semantic block sequence vectors, performing attention mechanism calculation on the semantic block sequence vectors to obtain semantic block relationship vectors, inputting the semantic block relationship vectors into a CRF layer for training, outputting a predicted classification result, calculating a loss function, adjusting model parameters of the initial abstract generation model based on the loss function, ending iteration, generating a text abstract model, inputting a text to be processed into the text abstract model, and outputting a text abstract. The application also provides a text abstract generation device based on semantic blocks, equipment and a storage medium. The application can make the abstract more concise and refined.

The invention provides a Vietnamese dependency syntax data enhancement method based on large language model collaboration, and belongs to the field of natural language processing. The method comprises the following steps of: generating an initial dependency syntax tree of Vietnamese sentences by using a plurality of current mainstream large language models as a multi-label model; by introducing an expert large language model GPT-4o-mini with a better analysis effect, scoring and feeding back a dependency tree output by each round, and driving a multi-label model to iteratively optimize until a set threshold value or a maximum round number is reached; and automatically selecting the dependency syntax tree with the highest score from the output of the three annotation models of each sentence to form a high-quality synthesized Vietnamese dependency syntax tree. And the syntactic analysis model is fused with an original UD Vietnamese training set, and training of the syntactic analysis model is completed. According to the method, high-quality automatic expansion of low-resource language Vietnamese dependency syntax data is realized, and the dependency analysis performance is effectively improved.

The present application relates to the technical field of natural language processing, in particular to a contract clause risk review method and system, comprising the following steps: obtaining a to-be-reviewed contract text, deconstructing the sentences of each contract clause to obtain a clause analysis feature set. The present application extracts the feature details of the syntactic tree depth, the reference distance, the logical nesting layer number and the professional term density by disassembling the contract clauses into basic semantic units sentence by sentence, uses the four types of index combinations in the clause analysis feature set to realize the measurement of the single clause analysis complexity score, and further aggregates the clause analysis complexity score to form specific indexes capable of quantifying the overall understandability risk of the contract. Meanwhile, the to-be-reviewed contract and the standard contract text library are executed for theme clustering and semantic weight distribution to clearly understand the actual focus of each theme, and the themes with larger semantic deviation are marked as missing risks.

The invention relates to the technical field of artificial intelligence and data analysis, and particularly provides a self-learning data analysis model recommendation method and system based on domain adaptation, and the method comprises the steps: dividing an input text into atomic analysis units through employing a syntactic tree analysis and paragraph topic clustering algorithm, pre-labeling entities in the atomic analysis unit based on a domain dictionary and a rule engine; according to the atomic analysis unit, outputting an analysis model recommendation result by adopting a semantic word segmentation optimization method, a multi-granularity retrieval matching method and a double-weight calculation sorting method; representing an association relationship between entities in the analysis model recommendation result based on an entity association graph; and performing structure updating on the entity association graph based on the user interaction behavior. According to the method, the text analysis accuracy, the semantic understanding depth, the recommendation precision and the dynamic updating capability of the knowledge base are remarkably improved, an efficient, accurate and self-adaptive solution is provided for data analysis in the vertical fields of medical treatment, finance, education and the like, and the method has important value and wide application prospects.

The invention discloses a text readability analysis method and system based on NLP processing and electronic equipment. The method comprises the following steps: extracting a plurality of character role entities; performing word segmentation processing on the preprocessed to-be-analyzed text to obtain a word sequence, and constructing a dependency syntax tree; on the basis of the character role entities, the preprocessed to-be-analyzed text and the dependency syntax tree, character number features, character character diversity entropy features, conflict density features, dialogue proportion features, relation density features and relation complexity features are extracted; performing feature processing and fusion on the role number feature, the role character diversity entropy feature, the conflict density feature, the dialogue proportion feature, the relationship density feature and the relationship complexity feature to obtain a fused feature vector; inputting the fused feature vector into an encoder to obtain a preliminary score vector; and determining a readability score of the to-be-analyzed text according to the preliminary score vector. According to the method, the accuracy of text readability analysis can be improved.

This invention relates to a multilingual syntax tree generation method based on multi-source syntax guidance and large-scale model collaborative optimization, belonging to the field of natural language processing. The method first generates a dependency syntax tree from the source language text using a traditional syntax analyzer, and performs quality assessment based on the reliability of the root node and dependency relations. For structures with low scores, the Qwen3-8B model is introduced for preliminary correction to improve the quality of the source tree. Subsequently, the segmented target language text and the quality-aligned source language syntax structure are used together as input prompts to guide multiple large-scale models to collaboratively generate the target language syntax tree, achieving cross-language syntax structure transfer. Finally, through consistency integration, rule compliance verification, and format standardization steps, high-quality, uniformly formatted pseudo-syntax tree data is generated. This invention can significantly improve the accuracy of low-resource language syntax analysis and the effectiveness and reliability of data generation standardization in low-resource dependency analysis tasks.

The application provides a sentiment element extraction method and system based on syntax-aware candidate matching, which specifically comprises the following steps: performing syntax analysis on input text to obtain a syntax tree to extract part-of-speech tags and phrase structures; extracting a candidate attribute word set from the syntax tree based on a preset attribute part-of-speech pattern, retrieving attribute sample examples from an example library according to text syntax vector representations corresponding to candidate attribute word phrase structures, and using the attribute sample examples to guide a plurality of large language models to generate reliable triplets; extracting a candidate opinion word set from the syntax tree based on a preset opinion part-of-speech pattern, retrieving opinion sample examples from the example library according to text syntax vector representations corresponding to candidate opinion word phrase structures, and using the opinion sample examples to guide the plurality of large language models to generate reliable quadruplets; and fusing the reliable triplets and the reliable quadruplets to obtain a sentiment element extraction result.

The method for mapping a noun phrase to a description logic concept based on epitaxy firstly exhaustively lists all text segments of the noun phrase, generates a mapping table of the text segments to resources in a knowledge base; then generates an analysis sequence according to the word segmentation, part-of-speech tagging and syntax tree of the noun phrase; and finally, according to the analysis sequence, continuously refines basic concepts generated by the indexed resources from the concept of EL++, until all words are analyzed, to obtain the description logic concept to which the noun phrase is mapped. The application can automatically process complex noun phrases containing implicit relations and generate high-quality description logic concepts through analysis of the syntax tree.

The present disclosure discloses a knowledge extraction method, system, device and storage medium. The knowledge extraction method comprises: obtaining and processing text data describing an object to determine a plurality of sentences and an object category corresponding to each sentence; generating a syntax relationship template for each sentence, the syntax relationship template comprising the part of speech of each word and the dependency relationship between the words; generating a syntax tree corresponding to each sentence through syntax analysis; matching the syntax relationship template and the syntax tree, and if the match is consistent, generating a candidate knowledge using the matched syntax subtree; using a knowledge prediction model to predict whether the candidate knowledge is correct knowledge; and using a knowledge classification model to process the predicted correct knowledge to identify the knowledge category corresponding to each piece of knowledge. The knowledge extracted according to the present scheme has the characteristics of low redundancy and high accuracy.

The embodiment of the invention provides a knowledge graph construction method and device based on PLC patent data, equipment and a medium, and the method comprises the steps: carrying out the standardization processing of structured patent data and unstructured patent data of a plurality of patent databases, obtaining initial standardized patent data, and carrying out the cross-data-source semantic mapping and unified coding processing; obtaining a standardized patent data set with a global entity identifier; performing entity labeling, disambiguation and alignment processing on the text data in the standardized patent data set based on an entity recognition model; generating a structured knowledge entity library according to the standardized patent data set and the processing result of the entity recognition model; according to the structured knowledge entity library, constructing a dependency syntax tree corresponding to each text sentence in the text data; determining a target entity triple based on the head word of each text sentence and the corresponding dependency syntax tree; and constructing a PLC patent data knowledge graph according to the target entity triples corresponding to all the dependency syntax trees.

The invention relates to the field of financial science and technology, in particular to the field of artificial intelligence, and particularly relates to a question separation method, device and equipment and a storage medium. The method comprises the steps that target phrases are traversed and matched in a domain knowledge tree to determine knowledge leaf node weights corresponding to the target phrases; in response to the fact that the number of the knowledge leaf node weights is at least two and the ratio of the maximum knowledge leaf node weight to the minimum knowledge leaf node weight is in a preset ratio range, matching the target word group in the syntactic tree to determine a syntactic leaf node weight corresponding to the target word group; responding to the condition that the number of the syntax leaf node weights is at least two and the ratio of the maximum syntax leaf node weight to the minimum syntax leaf node weight is in a preset ratio range, determining that at least two single questions exist in the customer questions; syntactic matching nodes obtained by matching the target phrases in the syntactic tree are determined, and client questions are separated based on the syntactic matching nodes to obtain single questions. The method and the device are convenient for improving the answering accuracy of the customer questions.

This invention relates to the field of character recognition and grammatical analysis technology, and discloses an automatic proofreading and error correction system for electronic publications based on artificial intelligence. The system includes: a data acquisition module for acquiring a sequence stream of text to be proofread; a syntactic parsing module for constructing a dependency syntax tree and identifying multi-level nested clauses; a structure processing module for generating feature projection vectors using feature space projection mapping when the syntactic nesting depth exceeds a threshold, thereby compensating for semantic features and maintaining logical closure; a verification module for determining the structural deviation index between the local syntactic matrix and the global semantic vector; and an error correction decision module for outputting error correction signals. This invention utilizes a feature projection compensation mechanism to fill in semantic gaps caused by local truncation, ensures the connectivity of long-range grammatical dependencies, eliminates false positives caused by loss of contextual information, and effectively enhances the system's parsing accuracy for ultra-long and complex sentences.

The invention discloses a multi-feature fusion ancient language named entity recognition method based on a large language model. The method comprises the following steps: extracting sentence semantic vectors by utilizing a pre-trained ancient language model GuwenBERT; sanza is adopted for dependency syntactic analysis, and structural features such as subject-called dependency, centering dependency and syntactic tree depth are extracted; the semantic features and the structural features are spliced to form fusion representation, and a similarity index is constructed based on FAISS to retrieve a small number of annotation examples; a large language model is guided to complete clue recognition, reasoning judgment and entity labeling in sequence through structured reasoning Prompt, and automatic recognition of personal names, place names, official names and time names in ancient texts is achieved. According to the method, the accuracy and robustness of ancient language named entity recognition are remarkably improved under the small sample condition, and an efficient technical scheme is provided for ancient book digitization and knowledge graph construction.

The application relates to a method, device and equipment for constructing a knowledge graph based on unsupervised syntactic analysis, comprising the following steps: obtaining a text data set, preprocessing the text data set, encoding the preprocessed data set, obtaining a vector representation of each phrase in a syntactic tree, calculating a score of the phrase vector representation, decoding a syntactic tree with the highest score as a result of syntactic analysis, and constructing a knowledge graph according to the syntactic tree with the highest score. The application solves the problems of large workload and poor portability in the prior art of constructing a knowledge graph by using heuristic rules, realizes construction of a knowledge graph based on unsupervised syntactic analysis, and has low cost and high accuracy.

The application discloses a Tibetan verb valence information extraction method based on a dependency syntax tree library, relates to the cross technical field of computational linguistics and natural language processing, and comprises the following steps: acquiring a Tibetan raw corpus; the source of the Tibetan raw corpus comprises literature, news, history, novels, teaching materials and a cloud Tibetan brat syntax tree library tool; preprocessing the Tibetan raw corpus to generate a dependency syntax tree library; analyzing the dependency syntax tree library to determine automatic verb valence information extraction rules; the automatic verb valence information extraction rules comprise zero-valence verb extraction rules, one-valence verb extraction rules, two-valence verb extraction rules and three-valence verb extraction rules; acquiring a Tibetan sentence to be extracted for verb valence information, and executing the automatic verb valence information extraction rules to obtain Tibetan verb valence information in the Tibetan sentence. The method improves the generation accuracy of Tibetan verb valence information.

The present application relates to the technical field of instruction analysis, in particular to a multi-stage alarm adaptive control method based on operation instruction analysis rules, comprising: constructing a dependency syntax tree and extracting instruction semantic triples by using a natural language processing model; mapping the triples to a semantic constraint graph to extract safety numerical boundaries, and dynamically correcting in combination with real-time environmental conditions; generating an instruction risk index by comprehensively considering parameter overrun and semantic deficiency probability, and triggering a hierarchical adaptive control according to the matched threshold value. The present application breaks the dependence of traditional industrial control on rigid protocols by constructing a multi-dimensional heterogeneous graph and a deep semantic analysis model, enhances the inclusiveness of non-standard instructions, and uses the risk vector module length to dimensionally evaluate the instruction risk, thereby realizing a multi-stage control strategy from intelligent semantic completion to interactive error correction guidance.

The present disclosure provides a multi-role dialogue method, device and electronic equipment, the method comprising: obtaining a plurality of target sentences; wherein the plurality of target sentences correspond to at least one role; encoding each target sentence into a corresponding syntax tree; encoding the role and syntax tree corresponding to each target sentence to obtain an encoding vector; decoding the syntax tree and the encoding vector to obtain a reply sentence of the plurality of target sentences.

The application discloses a kind of based on webpage body character attribute extraction method, comprising the following steps: S1, the relevant webpage of target character is obtained, the body text containing character attribute information is extracted from webpage, and text tokenization is carried out to body text;S2, according to character attribute, select the word attribute containing classification information;S3, build feature: use named entity, dependency relationship as additional word token mark, and build the state feature based on static word table, simultaneously using the context relationship of dependency syntax tree to build transition feature;S4, state feature and transition feature are used as the constraint of maximum entropy algorithm, and classification model is trained.The application obtains the body of webpage of target character of interest in complex network environment, realizes the method for extracting character feature in complex network environment by reasonably selecting and constructing feature using maximum entropy algorithm.