6 results about "Grammatical relation" patented technology

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 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 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.