102 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 relates to the technical field of natural language processing, in particular to an AI reading management and control method and system based on artificial intelligence, and the method comprises the following steps: carrying out text word segmentation processing on an input original text, segmenting the text into independent sentences, recognizing basic word units in each sentence, analyzing semantic adjacency relationships and syntactic structure features among vocabularies, and carrying out word segmentation processing on the word units; screening and extracting potential phrases representing paragraph meanings, and establishing a candidate semantic unit set; according to the method, through word segmentation, syntactic structure recognition and semantic adjacency analysis of the original text, potential phrases capable of representing paragraph significance are extracted, the candidate semantic unit set is constructed, and modeling of the semantic structure in the text is achieved. And associating the set with the reading fixation duration and the playback action of the user sentence by sentence to obtain a reading behavior response of each semantic fragment, and executing semantic weighting and reading behavior cross analysis according to the reading behavior response. Through the linkage mode, the semantic focus actually focused by the user at present can be recognized.

The invention discloses a teaching course recommendation method and system based on English learning data, particularly relates to the field of semantic processing, is used for solving the problem of poor pertinence of a traditional English learning course, and comprises the following steps: aiming at systematic difference of native language and English expression on a syntactic structure, constructing a language structure difference map and extracting a structure offset label; the method comprises the following steps: performing dependency syntactic analysis on semantic consistent sentence pairs of multi-language aligned corpora to generate a structural difference feature set; on the basis, a semantic-grammar dimension mapping graph is constructed in a classified mode, and a general structure expression vector is established for graph nodes. A teaching course is divided into knowledge point units in combination with a context label, and a mapping relation between a structure label and the course is established. The system performs structure analysis and semantic matching on sentences input by the learner, identifies structure migration type expression errors, and recommends accurate teaching content according to context and structure labels.

The invention discloses a combined entity relationship extraction method based on semi-supervised learning and a large language model. The system comprises three modules, namely a data enhancement module, a semi-supervised joint extraction module and a large-scale language model fine adjustment updating module. The data enhancement module (1) is used for implementing a double enhancement strategy on unmarked data, weak enhancement retains syntactic structures and topological features of entities and relationships, and strong enhancement adopts a large language model which is finely adjusted in advance to generate semantic equivalent disturbance; (2) a semi-supervised joint extraction module which obtains a preliminary extraction model through training of marked data, and then generates prediction on unmarked data based on the preliminary extraction model; (3) a large-scale language model fine-tuning updating module which realizes parameter-efficient large-scale language model fine-tuning by using low-rank self-adaption and dynamically adjusts through a semi-supervised extraction result; and (4) fusing prediction results of the three modules to obtain a final joint extraction model. According to the method, semi-supervised learning, a large language model and joint entity relation extraction are creatively combined, the problem of scarcity of artificial label data in the professional field of an existing joint entity relation extraction method is solved, more efficient information extraction is provided, and therefore the overall performance of a joint extraction model is improved.

The invention discloses a psychological counseling intelligent recommendation method and system based on artificial intelligence, and relates to the field of psychological counseling cross technology, and the method comprises the steps: calculating a three-dimensional emotion vector according to an emotion axis space and a syntactic structure, constructing a psychological semantic tensor by using a tensor outer product, generating a user intention tensor by using a prototype weight, and carrying out a flattening operation. Obtaining a final intention vector; combining the structural potential energy deviation and the spectral distance to construct a matching factor tensor; constructing a factorization machine model, predicting a matching score, generating a prediction score matrix, screening by using a Top-K selection method, generating recommended content, and constructing a visual interface to display the recommended content. According to the method, the fine granularity and accuracy of emotional representation are improved through emotional axis space and syntactic dependency analysis, the matching precision of intention and resources is improved through comprehensive calculation of semantic gravitation intensity and spectral distance, and the expression ability of matching factor tensor is enhanced through fusion of structural potential energy deviation and spectral distance.

The invention provides a self-adaptive perception event element extraction method, which comprises the following steps of: firstly, acquiring data from open source resources of multiple fields, cleaning and labeling to construct a high-quality multi-field event element extraction data set, and then extracting event elements from the multi-field event element extraction data set by utilizing the constructed multi-field event element extraction data set. According to the method, two different types of event element extraction models are trained and finely adjusted, the two different types of models are a traditional deep learning model and a large model, then the length, syntactic structure, dependency relationship and vocabulary richness characteristics of a sentence are analyzed, complexity evaluation indexes are constructed, and the complexity of the sentence is evaluated. The method comprises the steps of automatically determining the complexity level of each sentence on the basis of the indexes, classifying the sentences, finally selecting a self-adaptive model according to the sentence complexity evaluation result, and finally integrating the result and outputting the result. By means of the scheme, self-adaptive extraction model selection is achieved, and the accuracy and efficiency of event element extraction are improved.

The invention relates to the technical field of copywriting analysis. The intelligent copywriting analysis method comprises the steps that based on user input information, an analysis task is generated, a task identifier is distributed to the analysis task, the analysis task is stored in an analysis storage database, meanwhile, the state of the analysis task is set to be an initial state, and the analysis task is stored in the analysis storage database; the initial state comprises to-be-executed or in-execution; when the state of the analysis task is to be executed, determining the execution opportunity of the analysis task based on a preset task scheduling strategy, and updating the state of the analysis task to be in execution; when the state of the analysis task is in execution, reading corresponding user input information from an analysis storage database according to the task identifier; semantic features and syntactic structures of information input by a user are extracted, key information in a text is recognized, and a corresponding analysis task type is matched according to the key information; and obtaining an analysis calculation result. The method has the effect of improving the copywriting analysis efficiency.

The invention belongs to the technical field of computers and text data processing, and particularly discloses an ancient book text-based information extraction method, a program product and electronic equipment, and the method comprises the following steps: obtaining a target ancient book text of which information is to be extracted; determining dependency syntactic structure information of the target ancient book text as target dependency syntactic structure information, the dependency syntactic structure information being used for reflecting grammar and semantic relationships between words in the target ancient book text; based on the target dependency syntax structure information, target information is extracted from the target ancient book text through a pre-trained ancient book text information extraction model, and the target information comprises idiom information, and / or object information, and / or emotion information. Through the technical scheme provided by the invention, the accuracy of ancient book text information extraction can be improved.

The invention discloses an English emotion intonation reading device, which relates to the technical field of English emotion intonation reading and comprises a text input module, a semantic analysis module, an emotion recognition module, an intonation adjusting module, a speech synthesis module, an audio output module and a self-learning module. The text input module is used for acquiring an English text by typing, uploading a document and adopting an OCR (Optical Character Recognition) image recognition mode to obtain original text data; the semantic analysis module is used for analyzing original text data by adopting a method for analyzing a syntactic structure, keywords and context information to obtain semantic information of a text; the emotion recognition module is used for processing semantic information of the text in combination with a rule base and a deep neural network, recognizing emotion categories in the text and obtaining emotion tags; and the intonation adjusting module is used for adjusting voice expression parameters in the emotion label by adopting a strategy of controlling the speed, pitch, rhythm, pause and accent to obtain a voice regulation and control scheme.

The invention provides a software development system management method and system based on big data. A code review data set in remote collaboration software development is collected, a review knowledge graph is constructed based on time-space correlation characteristics of the data set, and graph nodes comprise review behavior modes correlated with geographical and time characteristics. Syntactic structure features of current code change are synchronously extracted, the syntactic structure features and historical review tracks in the knowledge graph are jointly coded, and a processing instruction containing version difference analysis priorities is generated. And determining an allocation strategy of the cross-regional review task by dynamically matching the processing instruction and the domain knowledge coverage range. And finally, according to a mapping relationship among the distribution strategy, the version priority and the quality evaluation node, generating an examination management regulation and control strategy adaptive to the software development system. According to the technical scheme provided by the invention, the management efficiency and precision of the software development system can be improved.

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 data processing, in particular to an entity recognition method and device, equipment and a medium. In the embodiment of the invention, three independent feature extraction channels are analyzed through a BERT model, a convolutional neural network and a dependency syntax, a first feature vector, a second feature vector and a third feature vector are determined, the obtained three feature vectors are integrated, and a finally obtained target feature vector comprises deep semantic information and context information. The method has the advantages that feature fusion is realized, local features and syntactic structure information are also included, flexibility and expression ability of feature fusion are enhanced, complex text understanding and entity recognition ability of a named entity recognition model are remarkably improved in the entity recognition process, and the entity recognition effect and accuracy are improved.

The invention relates to the technical field of natural language processing, and discloses a knowledge injection and graph neural network-based open domain text information extraction method, which comprises the following steps of: extracting all noun phrases from input text data to construct a candidate entity set; combining the candidate entities in pairs, and constructing a self-attention incidence matrix of each entity pair; performing sequence sampling on the self-attention incidence matrix to generate a candidate triple sequence set; calculating semantic similarity between the candidate triple sequence and the input text data, and outputting the first k high-correlation triple sequences as initial information extraction results of the input text data; and performing dependency structure analysis on the initial information extraction result based on a graph neural network, and generating a triple sequence through redundant sequence labeling as a final information extraction result. According to the method, the recognition rate of the complex syntactic structure triad in the open domain information extraction task is remarkably improved, and meanwhile, the redundancy of the extraction result is effectively reduced.

The invention relates to an English translation training method based on a multi-modal corpus, and the method comprises the steps: collecting text, voice, image and context scene information data, constructing a multi-modal parallel corpus, and generating enhanced training data through a cross-modal alignment technology; a dynamic sampling strategy is adopted, related corpora are automatically weighted according to a field input by a user, and field adaptability is optimized; designing a mixed loss function, and performing joint training in combination with semantic similarity, syntactic structure and cross-modal consistency; an online feedback mechanism is introduced, and a corpus and model parameters are updated in real time through user error correction data. Through the multi-modal corpus and the dynamic training strategy, the problems that a traditional translation model is poor in field adaptability and high in data dependence are solved, and the accuracy, robustness and cross-field adaptability of the translation model are improved.

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 invention discloses a grammar error correction method based on large model syntactic preference optimization. According to the grammar error correction method, syntactic relevance between retrieval example sentence pairs and syntactic relevance between the retrieval example sentence pairs are cooperatively utilized. On one hand, a Monte Carlo tree search algorithm is used for exploring the influence of syntactic structure differences among retrieval examples on error correction performance, search path selection is adjusted based on dynamic weights, and syntactic perception corpora containing hidden syntactic association are constructed. On the other hand, by constructing a syntactic preference alignment mechanism, syntactic knowledge contained in syntactic perception corpora is effectively utilized, dynamic adjustment of grammar error correction model parameters is achieved, and then the performance of the grammar error correction model in a complex grammar error correction task is improved. According to the method, a comprehensive contrast experiment is carried out on two Chinese error correction data sets and two English error correction data sets. Experimental results show that the method can effectively integrate the structured syntactic features in the retrieval examples, and effectively relieve the over-correction phenomenon of the grammar error correction model.

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 present application discloses a method for calculating sentence similarity, a related device, and a computer storage medium, wherein the method includes: obtaining two sentences to be compared and a decay factor; vectorizing phrases in the two sentences to be compared respectively to obtain a word vector for each phrase in the two sentences to be compared; determining the word vector similarity between the two sentences to be compared based on the word vector of each phrase in the two sentences to be compared; generating a syntactic structure binary tree of each sentence to be compared according to a preset sentence structure division rule; and determining the sentence similarity between the two sentences to be compared based on the decay factor, the syntactic structure binary tree of each sentence to be compared, and the word vector similarity between the two sentences to be compared. Compared with the prior art method of calculating the similarity between sentences based only on the syntactic structure binary tree, the present application is more accurate.

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.

Embodiments of the present invention provide a method and apparatus for text sentiment classification, relating to the field of data processing technology. The method comprises: extracting, for each character in a text to be classified, character semantic information representing the semantic meaning of the character based on the character's position in the text to be classified; obtaining, based on the position of each character in the text to be classified, syntactic structure information representing the syntactic structure of the text to be classified; and determining, from preset sentiment categories, the target sentiment category to which the sentiment expressed in the text to be classified belongs based on the character semantic information and the syntactic structure information of each character. Application of the embodiments of the present invention enables classification of the sentiment category of the sentiment expressed in the text.

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 belongs to the field of text labeling, and particularly relates to a Chinese electronic medical record relation extraction method based on a graph attention network, which comprises the following steps: labeling unlabeled data of an electronic medical record; preprocessing the Chinese electronic medical record data set to obtain part-of-speech tagging and syntactic analysis information; the characters are mapped into vector and label embedded representations, and splicing is carried out; a Bi-GRU network is used for reading the sequence from the forward direction and the backward direction, and a state vector is used for capturing context information to obtain coded representation of the whole sequence; the method comprises the following steps: constructing a dependency syntax dependency tree by using a GCN, and then constructing an adjacent matrix for an input dependency syntax analysis tree by adding a virtual edge by using a graph convolutional network; potential relation prediction and global correspondence are carried out, and finally relation triples are extracted. According to the method, syntactic structure information and global semantic information are fully fused, and the accuracy and robustness of relation extraction are improved.

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.

The invention discloses a text sentiment prediction processing method, a server, a storage medium and a program product, and relates to the technical field of computers, the text sentiment prediction processing method comprises the steps of analyzing a text sequence syntactic structure, constructing a syntactic structure tree and generating syntactic structure features, and based on an attribute word sequence and the syntactic structure features, accurately capturing semantic association between attribute words; a first semantic feature is output by means of a pre-trained first language model, the local semantics of each segmented word is captured through a syntactic graph attention network, and the relation feature of each attribute word is captured through a relation graph attention network, so that superiority can be displayed on alignment between the attribute word and the segmented word for identifying the emotion; therefore, the accuracy of text sentiment prediction is improved.

The present application is applicable to the field of natural language processing technology, and provides a method and device for text sentiment analysis. The method includes: determining the sentiment tendency strength value of a first sentiment word based on the number of times the first sentiment word appears in a corpus; extracting a third sentiment word having a similar syntactic structure to the second sentiment word from the corpus based on the second sentiment word in the first sentiment dictionary; generating a second sentiment dictionary based on the sentiment tendency strength value and the third sentiment word, the second sentiment dictionary including the first sentiment word, the second sentiment word and the third sentiment word; analyzing the sentiment polarity of the test text based on the second sentiment dictionary. The present application can improve the coverage and accuracy of sentiment polarity analysis of the test text based on the sentiment dictionary.

The present invention discloses an event element extraction system, comprising: an input layer for converting a sentence into a vector sequence containing multiple features to enrich the feature expression of the sentence; a Bi-GRU encoding layer for encoding information in both forward and backward directions along the words in the sentence to capture the Context representation of the input text; a graph TransformerEncoder layer for modeling syntactic distance, converting the sentence into a graph structure by using the dependency syntactic structure, and calculating the attention with syntactic distance information added on the graph structure; and an output layer for predicting event elements and their roles as classification results. An event extraction method mainly comprises the following four steps. The present invention provides an event element extraction method integrating bidirectional encoding and graph TransformerEncoder. A TransformerEncoder structure is added to the graph neural network to learn the dependencies between words at different distances, and a syntactic modeling strategy is added to the attention mechanism of the TransformerEncoder to utilize syntactic distance information to improve the performance of event element extraction.