13 results about "Grammatical relation" patented technology

The invention relates to the technical field of natural language processing, in particular to an automatic text proofreading system and method based on natural language processing, and the system comprises a text preprocessing module, a grammar topology analysis module, a domain term dynamic adaptation module, an error association graph construction module and an intelligent correction generation module. Wherein the text preprocessing module is used for receiving original text input and performing sentence segmentation processing; the grammar topology analysis module is used for constructing a grammar relation tree; the domain term dynamic adaptation module is used for checking term consistency; the error association graph construction module is used for generating an error association graph; and the intelligent correction generation module is used for generating a final proofreading text based on a correction priority algorithm. According to the method, a processing mechanism combining grammar topology analysis and term consistency verification is introduced, the error correlation graph with the weight is constructed, the proofreading text is generated based on the correction priority strategy, and accurate recognition and orderly correction of multiple types of errors are achieved.

The invention relates to the technical field of text proofreading, in particular to an intelligent text proofreading method and system based on cognitive grammar, and the method comprises the following steps: collecting semantic expressions such as verb affairs, matching a standard graph expression for verification, executing word order reconstruction, limiting pronoun pointing, and automatically generating a proofreading result. According to the method, a semantic structure label system is established by collecting verbs, position words and grammatical relations, classification and integration of sentence pattern semantics are achieved, consistency verification is conducted by matching cognitive structure library standard graphic configuration, semantic mismatch structure structures can be recognized and recombined, potential fracture links can be positioned by tracking word semantic mutation points, and the recognition accuracy is improved. The word order is reconstructed and optimized to achieve ordered expression of the logic relation, the grammar defect position is defined through difference comparison between the reconstructed fragment and the correction item, the pronoun logic definition and sentence pattern expression accuracy are enhanced, the recognition and correction capacity of hidden semantic errors in diversified texts is improved, and the text proofreading effect is greatly improved.

The present application relates to the technical field of natural language processing, in particular to an implicit sentiment analysis method based on syntax enhancement and context awareness, which solves the technical problems that the existing analysis method ignores the difference of sentiment contribution of different syntax relations and easily introduces irrelevant semantic noise, etc., which encodes the word vector of the target sentence and its context sentence to obtain the text semantic feature of the target sentence and the text context feature of the target sentence; a syntax dependency relation weight matrix is constructed to fuse the text semantic feature and the syntax feature of the target sentence to obtain the syntax semantic joint feature; the cosine similarity of the target sentence and its context sentence is calculated and the context features are weighted and aggregated to obtain the aggregated context features; the local to global sentiment reasoning is realized by using the self-attention mechanism, the gated fusion and the hierarchical feature fusion framework based on bidirectional LSTM; the sentence-level global feature is input into the full connection layer to predict the sentiment polarity probability distribution of the target sentence, and finally the sentiment label category of the target sentence is obtained.

The application discloses a text classification method, device and electronic equipment. The method comprises the following steps: obtaining a text to be classified, wherein the text to be classified carries a primary label and a secondary label; performing word segmentation on the text to be classified to obtain a plurality of first words corresponding to the text to be classified; determining a main component word of the text to be classified from the text to be classified according to the grammatical relationship between the first words in the text to be classified; obtaining a first semantic vector of the main component word, wherein the first semantic vector indicates the semantics of the main component word; more reliable information can be extracted for the classification of the text to be classified by obtaining the main component word in the text to be classified; the meaning expressed by the text to be classified can be known in a more fine-grained manner by obtaining the semantic vector of the main component word in the text to be classified; and finally, the first class label to which the text to be classified belongs is determined from the primary label and the secondary label based on the first semantic vector, thereby improving the classification accuracy of the text to be classified.

The present application discloses a text processing detection method, apparatus, device, and computer storage medium. The method includes: obtaining a first text; determining a target text segment including a quantifier from the first text; performing dependency grammar analysis on multiple words in the target text segment according to the parts of speech of the multiple words in the target text segment to obtain a first dependency grammar relationship between each word in the target text segment; constructing a dependency grammar relationship tree according to the first dependency grammar relationship; determining at least one target dependency grammar relationship structure that matches a preset dependency grammar relationship structure from the dependency grammar relationship tree; and determining a target semantic relationship structure corresponding to the at least one target dependency grammar relationship structure according to the one-to-one correspondence between the preset dependency grammar relationship structure and the preset semantic relationship structure. By using the text processing method, apparatus, device, and computer storage medium provided by the present application, the semantic relationship between a quantifier and other words can be accurately determined.

The invention relates to a statement conversion method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an initial statement to be subjected to semantic conversion; the initial statement is a statement which can be identified by the first database; performing grammar recognition on the initial statement, and constructing a grammar tree of the initial statement; nodes in the syntax tree represent statement vocabularies in the initial statement; the node information of different nodes comprises grammar information of corresponding statement vocabularies in the initial statement; the relationship among different nodes is a grammatical relationship among corresponding statement vocabularies; according to semantic types of different nodes in the syntax tree, performing semantic conversion on the syntax information in the syntax tree to obtain an abstract syntax tree; performing synonymous conversion on the node vocabularies in the abstract syntax tree to obtain a target statement; the target statement is a statement which can be identified by the second database. By adopting the method, statement conversion can be accurately carried out.

The present disclosure relates to the technical field of information retrieval, and discloses a method for constructing an entity relationship graph, including: performing entity recognition and an operation of uniting like terms on the text to be processed to obtain a standard text entity set; performing grammatical structure analysis on the text to be processed, to obtain a grammatical relationship graph; selecting the node connected to the connection edge in the grammatical relationship graph as a target connection node and performing vector conversion to obtain a text vector, and analyzing a text entity relationship between the target connection nodes according to the text vectors; constructing a knowledge graph according to the standard text entities and the text entity relationship to obtain the entity relationship graph corresponding to the text to be processed. The present disclosure can improve the accuracy of the text entity relationship graph.

The application relates to a commodity recommendation method and device, equipment and medium in the e-commerce technical field. The method comprises the following steps: obtaining a commodity title of a commodity, constructing a corresponding candidate phrase set after performing word segmentation on the commodity title; determining whether there is a grammatical relationship between word elements in each candidate phrase in the candidate phrase set; when the grammatical relationship exists, determining a grammatical structure to which the corresponding grammatical relationship belongs; determining the similarity between each candidate phrase belonging to the same grammatical structure and the commodity title; and screening out the candidate phrase with the similarity satisfying a preset condition as a matching keyword of the commodity for commodity recommendation for each grammatical structure. The application combines the grammatical information of phrases to deeply mine multi-dimensional keywords of commodities for commodity recommendation, and can improve the accuracy of recommended commodities.

The invention discloses a method and a device for programming based on a local code warehouse driven by a large model, and relates to the technical field of computers, according to the method, knowledge in a code library is organized into a plurality of communities through hierarchical processing of an atlas, each community comprises communities with similarity, and the communities also comprise similar nodes. The hierarchical structure not only retains the grammatical relationship between codes, but also introduces the similarity of the semantic level through the construction of communities and communities. By introducing the similarity of the semantic level, the retrieval precision and the coverage rate can be improved, and it is ensured that the reference code not only can contain a code snippet similar to the code to be processed in grammar, but also can contain a code similar to the code in semantic and functional aspects. On the basis, the target large model conducts reasoning on the reference code, the to-be-processed code and the task instruction, the requirement and intention of the to-be-processed code can be more accurately understood, a higher-quality target result is obtained, and a developer is assisted in rapidly and accurately achieving the programming target.

The embodiment of the invention relates to a speech synthesis method and device, computer equipment and a storage medium. The method mainly comprises the following steps of: analyzing sentiment semantic information and an inter-word dependency relationship of text data to be synthesized by adopting a graph sentiment semantic encoder, and performing sentiment semantic enhancement to obtain sentiment semantic features; converting the to-be-synthesized text data into phoneme data; performing grammar relation coding on the text data according to the grammar graph of the to-be-synthesized text data by adopting a graph coder to obtain relation coding characteristics, and performing text coding on the phoneme data by utilizing the relation coding characteristics based on a graph attention mechanism to obtain text coding characteristics; splicing the emotion semantic features and the text coding features to obtain spliced features; performing time length prediction on the splicing features to obtain prediction data; decoding the prediction data to obtain spectrum data to be synthesized; and converting the spectrum data to be synthesized into synthesized audio. By adopting the method, the accuracy and emotion expression of speech synthesis can be improved.

The invention discloses a multi-source data-based staphylographic large model pre-training method and system, and the method comprises the steps: collecting multi-source staphylographic texts of news, social contact, government affairs, academic and the like, and extracting the vocabulary coverage and language style features, thereby achieving the layering of corpus quality and the recognition of variants; analyzing a flection change structure of the staphylococcus by adopting a word form reduction and affix separation technology, and generating a training sample unit carrying a form label; high-value mask positions are identified based on morphological complexity, word-level, phrase-level and sentence-level mask tasks are configured, and learning of grammatical relationships such as subject-model consistency and name-form consistency is enhanced in combination with a morphological consistency constraint enhancement model; a course learning strategy is adopted to progressively execute training tasks according to difficulty, finally, optimal parameters are screened through multi-dimensional evaluation to output a staphylography pre-training model, and a high-quality language representation basis can be provided for downstream tasks such as staphylography text classification, named entity recognition and machine translation.

The invention provides a text quality detection method, electronic equipment and a program product, and relates to the technical field of computer technology and natural language processing. The method comprises the following steps: performing structured analysis on an input text, extracting language components, grammatical relationships and entity information, and constructing a semantic tree structure based on the language components, the grammatical relationships and the entity information; constructing a causal atlas based on the semantic tree structure, and allocating weights for causal edges in the causal atlas according to the multi-layer time sequence consistency tensor to obtain a weighted causal atlas; based on the weighted causal atlas, calculating a logic consistency score; when the logic consistency score is lower than a preset score threshold value, according to the multi-layer time sequence consistency tensor, a causal relationship with logic conflicts is positioned, conflict types are recognized, a conflict causal chain containing the conflict types and positions is obtained, and the conflict types comprise time conflicts, logic conflicts or context conflicts. According to the embodiment of the invention, the detection accuracy can be improved.