39 results about "Word order" patented technology

The invention provides a real-time translation method and interaction system based on streaming voice segmentation and semantic verification. The method comprises the following steps: receiving a conference audio stream, identifying effective audio frames based on dual-channel voice activity detection, and pressing the effective audio frames into a dynamic buffer area; according to a preset dynamic segmentation strategy, outputting an initial voice segment from the dynamic buffer area for voice recognition, and obtaining a corresponding initial text segment; performing multi-stage reliability verification on the initial text fragment, and dynamically correcting or complementing the initial text fragment according to a verification result to obtain a reliable recognition result; and after the reliable identification result is obtained, triggering an asynchronous parallel translation task. According to the method, a semantic correction mechanism cooperating with the adaptive truncation depth is designed, so that the semantic fragmentation problem of the long-sequence audio during streaming truncation is solved, and the translation accuracy of a complex word order language is greatly improved while low delay is ensured.

The invention relates to a railway traffic field semantic recognition method and device based on few sample data and a medium, and the method comprises the steps: carrying out the preprocessing of the obtained railway traffic field data, and constructing a field dictionary and a rule base; converting the preprocessed domain data into enhanced voice, forming a training pair by the enhanced voice and a corresponding text, and performing domain fine tuning on the ASR model; inputting a voice instruction in the railway traffic field into the field fine-tuned ASR model, and outputting an ASR initial transcription text; on the three levels of sentence level, local word order level and semantic tag level, word legality judgment is performed on the ASR initial transliteration text through a multi-scale model, and a high-credibility anchor point set and uncertain content are recognized; and field semantic rewriting is carried out based on the legitimacy judgment result of the words, and finally a structured instruction text is output. Compared with the prior art, the method has the advantages of realizing accuracy and adaptability of semantic recognition in the field of railway traffic and the like.

This invention discloses a product classification and recognition method and system based on AI technology, belonging to the field of AI recognition technology. By constructing a semantic alignment master vector Edis, the system achieves structured extraction and ambiguity resolution of the overall semantic representation of the product. On this basis, the system introduces the construction of a product classification label semantic vector set Gvec and a semantic similarity score set Sset, enabling the system to perform fine-grained matching of multiple classification labels based on contextual semantic features. This constructs a confidence score Conf reflecting the classification credibility, which is then compared with a preset classification judgment threshold Thrs to ensure that product labels are output only when the classification judgment credibility is sufficient. Through the connection of the above processes and the linkage of output results, this method not only significantly reduces the probability of misjudgment caused by expression ambiguity and word order reversal, but also provides a reliable rejection mechanism when accurate classification is not possible, effectively improving the overall robustness and practical value of the classification system.

This invention relates to the field of information retrieval structure technology, specifically to a method and system for monitoring data during the investment promotion process in industrial parks. The method includes the following steps: extracting investment promotion text, identifying combinations of verbs and resource nouns, analyzing word order and semantic features, filtering action-driven segments, dividing structural sequences and identifying connection relationships, establishing semantic jump paths, reorganizing sentence order, and generating a data monitoring structure template for investment promotion in industrial parks. In this invention, by extracting verbs and resource nouns to construct sentence content, combining word order features and semantic tags to classify and filter segments, dividing structural sequences based on the position of resource nouns and merging content with consistent semantic direction, mapping connectors to jump paths to construct task behavior sequences, and realizing the logical organization and sequential reorganization of sentences, this generates a data expression method with traceability and structured features, improving the semantic recognition efficiency and data monitoring capabilities during the investment promotion process in industrial parks.

The embodiment of the invention provides a natural language processing method and equipment, and the method comprises the steps: obtaining a lexical item set corresponding to a to-be-processed initial input statement, and enabling the lexical item set to comprise an initial lexical item included in the initial input statement, and an extended lexical item of the initial lexical item; performing word order reduction on lexical items in the lexical item set to obtain an extended statement of the initial input statement; and obtaining a processing result based on the extension statement and a natural language processing model. According to the embodiment of the invention, the initial lexical item and the extended lexical item included in the initial input statement can be acquired and restored into the extended statement with a smooth word order, so that the initial input statement can be extended and enhanced, and the obtained extended statement can be better understood by a natural language processing model; and then natural language processing is performed in combination with the natural language processing model, so that the accuracy and stability of a processing result can be improved.

This invention discloses a speech prosody recognition method, system, device, and storage medium. First, the voice from a customer service call is captured to obtain a dialogue speech signal. Then, the dialogue speech signal is preprocessed to obtain a preprocessed dialogue speech file. Next, the preprocessed dialogue speech file is vectorized to obtain a corresponding feature matrix. The feature matrix is ​​input into a trained prosody model to obtain the model calculation result. Then, based on Mandarin and dialect templates, corresponding template thresholds are obtained. Using the model calculation result and template thresholds, the prosody recognition result of the feature matrix is ​​obtained. Finally, based on the prosody recognition result, the feature matrix is ​​processed by text mapping to obtain the dialogue word order text. This invention effectively improves the recognition accuracy and efficiency for speech containing dialects.

Embodiments of the present disclosure provide a method and a device for processing natural language. The method comprises obtaining a set of terms corresponding to an initial input sentence to be processed, wherein the set of terms comprises initial terms included in the initial input sentence and extended terms of the initial terms; performing word order restoring on terms in the set of terms to obtain an extended sentence of the initial input sentence; and obtaining a processing result based on the extended sentence and a Natural Language Process (NLP) model.

This application provides a radar text processing method and related apparatus. The method includes: processing the radar text to be processed to obtain a first word vector set; extracting contextual semantic features from the first word vector set to obtain a first semantic feature set; extracting word features from the first word vector set to obtain a first part-of-speech feature set; weighting the first part-of-speech feature set with the corresponding semantic features based on importance to obtain a second part-of-speech feature set; concatenating the second part-of-speech feature set and the first word vector set according to the word order of the radar text to be processed to obtain a second word vector set; extracting contextual semantic features from the second word vector set to obtain a second semantic feature set; and performing label classification processing on the second semantic feature set to obtain a classification label corresponding to each first word vector in the first word vector set, thereby improving the accuracy of word classification processing in radar text.

The invention relates to the technical field of machine question answering, in particular to a personalized health question answering system based on reinforcement learning, which comprises an input positioning module, an anaphora freezing module, a state injection module, a path screening module and a content splicing module. According to the method, the fuzzy region is marked by identifying semantic features such as subject separation, pointing missing and intention interruption, and unstable content is frozen and screened by combining semantic chain consistency, word order change and context distance judgment; the internal structural integrity and semantic closure of the statement are enhanced by means of subject-called structure reconstruction, verb-object combination recognition and modification chain judgment, and semantic tripping content is further eliminated through subject affiliation matching, upper and lower concept connection and logic consistency screening. Content recombination and linkage optimization are realized based on objective word positioning, tail word sequence adjustment and semantic ending complementation, and semantic undertaking stability, content coherence and reply pertinence in ambiguous input, structure disorder or multi-turn jump scenes are improved.

The invention discloses a method and device for recognizing and reconstructing vector characters of a CAD drawing, and relates to the technical field of data processing. The method comprises the steps that a PDF file is analyzed, and vector elements and attribute information of the vector elements are extracted; character stroke candidate lines are screened out according to attribute grouping; carrying out connectivity analysis and density-based spatial clustering on the lines, and identifying a text region; rendering the vector lines of the text region into an image with high resolution, carrying out single character segmentation, and recognizing characters by combining drawing-level font library template matching with a single character recognition model; combining the single characters into word groups according to the positions and the context information, and performing word order correction by adopting a language model; the recognition result is written back into an editable CAD text object; according to the method, vector geometric information is directly utilized, distortion of bitmap sampling is avoided, the small character recognition rate and the anti-jamming capability are effectively improved, and end-to-end semantic recovery from vector lines to searchable and editable texts is 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 invention relates to a member report generation method and device based on a large language model, and a medium. The method comprises the following steps: S1, receiving natural language input of a user and automatically decomposing the natural language input into a plurality of independent subtasks; s2, dynamically generating a structured step-by-step execution plan for each subtask; s3, constructing a special agent instance based on the step-by-step execution plan, calling a data tool according to a distributed execution plan sequence, integrating a large language model to carry out context understanding, and outputting a query result; s4, performing semantic verification based on the user intention on the query result by using a large language model, turning to S5 after the verification is passed, otherwise, turning to S2; and S5, aggregating query results of the plurality of subtasks, extracting key information from the query results, generating a structured data report, adjusting word order and expression, and outputting the structured data report in a dialogue type reply form. Compared with the prior art, the large language model is used for conducting semantic verification on the query result, and the accuracy and integrity of the query result are guaranteed.

The present disclosure relates to the technical field of computers, in particular, to a word-based video recommendation method and device, medium and electronic equipment. The method comprises: determining a plurality of words to be learned and a word order of the words to be learned according to the words in a word book; determining a target definition of the words to be learned, and determining a learning video corresponding to each word to be learned according to the target definition; and displaying the learning video corresponding to each word to be learned according to the word order. In this way, for each word to be learned, the user can focus on understanding and learning the application of the word to be learned under the target definition, thereby improving the mastery and application of the word to be learned. In addition, through the display of the learning video, the real usage scenario of the word to be learned can be restored, effectively improving the user's attention concentration in learning the word, improving the user's learning pleasure, reducing the boredom of word learning, and further accelerating the speed of mastering the word.

The invention relates to the technical field of cloud collaboration, in particular to a network false information detection method and system based on large model fine tuning, and the method comprises the following steps: extracting reference misplaced text and context information, judging semantic span to form an association set, separating a fact sentence, an emotion sentence and an origin sentence to track a semantic chain, and comparing an original text to recognize an offset phrase. And adjusting a word order analysis causal relationship to obtain a false information judgment conclusion. According to the method, by means of association extraction and chain type tracking of the dislocation relation between semantic fragments, the logic sequence can be reconstructed on the statement level, semantic offset caused by paragraph jump can be eliminated, content causal connection and context coherence can be kept, and the corresponding relation of fact statements, emotion tendencies and time elements can be synchronously recognized in semantic path analysis; semantic judgment is kept continuous and stable, dynamic comparison and offset correction of reference content and original context are completed in a multi-source corpus scene, and false information identification stability and conclusion reliability are improved.

The invention discloses a relay protection constant value checking method, device and equipment based on multi-feature matching, and relates to the technical field of safe and stable operation of a power grid, and the method comprises the steps: calculating a plurality of similarities between an interrogation measured value set from a relay protection device and each constant value item in a standard constant value list; weighted fusion is carried out on a plurality of similarities of word forms, word orders, sentence lengths, semantics and component relations to obtain comprehensive similarities, an optimal allocation algorithm is combined, a unique matching item is searched in a standard constant value list for each constant value item in an interrogated constant value set, a matching pair set is formed, and the matching pair set is used for matching the word forms, the word orders, the sentence lengths, the semantics and the component relations. And performing depth comparison on each pair of constant value items in the matching pair set to obtain a constant value comparison result. According to the method, false alarms caused by naming differences are reduced through multi-feature matching, so that the accuracy of a constant value comparison result is improved.

The invention discloses a multi-stage text irrigation recognition method and system based on rules and deep learning, a medium and a product, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the preprocessing of a target recognition text, and obtaining an intermediate text; performing rapid screening on the intermediate text based on a preset rule, and judging whether the screening is passed or not; if not, it is judged that irrigation exists; if yes, performing disordered word order detection on the intermediate text to obtain a word order score; according to the word order score and a preset word order score threshold value, whether the word order disorder problem exists in the intermediate text or not is judged; if not, it is judged that irrigation does not exist; if yes, inputting the intermediate text into a preset irrigation detection model for irrigation detection to obtain an irrigation probability; judging whether the irrigation probability is greater than a preset probability threshold; if yes, judging that irrigation exists; if not, it is judged that irrigation does not exist. According to the method, the overall detection efficiency is remarkably improved, and efficient and accurate recognition of the text irrigation behavior is realized.

The invention relates to the technical field of data arrangement, in particular to a digital archive arrangement system based on semantic analysis, which comprises a hierarchical extraction module, a semantic evaluation module, a co-occurrence phrase recognition module, a phrase merging module and an arrangement output module. According to the method, numerical combination and hierarchical mapping are carried out on the structural features of the first row of the field, the nesting hierarchy and the structural position of the field in the document can be accurately determined, confusion interference to different semantic hierarchies is avoided, keywords with high semantic credibility are screened out in a word segmentation statistics and field structure linkage mode, and the semantic reliability of the keywords is improved. Dynamic recognition and accurate extraction of information density and semantic weight are achieved, co-occurrence structure extraction of keywords in cross-field context is combined, the recognition capability of semantic association content is enhanced, vectorization and similarity measurement are carried out on the word order, context and appearing field features of high-frequency keywords, and the semantic association content recognition efficiency is improved. Redundant or semantically repeated keyword combinations are effectively combined, and semantic representativeness of extracted phrases is ensured.

This invention relates to the field of machine learning technology, specifically to a method and system for intelligent spatial data analysis and decision support based on a large language model. The method includes the following steps: acquiring spatial entities, location phrases, and task descriptions; analyzing word order and modification relationships; extracting task elements and action sequences; constructing continuous semantic paths; identifying geographic terms and range changes; extracting statements whose angle between action instruction direction vectors is less than a preset threshold; and obtaining a set of spatial action structures. In this invention, a semantic task list is constructed by extracting spatial entities, location phrases, and task content. Actions are ordered by combining action words and word order relationships. The connection methods between segments are analyzed, and a coherent task path is organized. The sequential matching and semantic connection between spatial objects and actions are completed. By filtering the information structure of action direction and location changes, task statements are arranged in an orderly manner according to the process, avoiding ambiguity and interruptions in instruction transmission, and maintaining the orderly integration and flow of spatial information.

The application relates to the technical field of semantic processing and knowledge graph, and proposes an AI-based intelligent question answering method and system, which comprises the following steps: collecting a knowledge graph of an intelligent question answering library and obtaining inquiry text of a user; obtaining a plurality of words by performing word segmentation on the inquiry text; obtaining a reply design scale of the inquiry text; determining the number of analysis queues and establishing a plurality of analysis channels; obtaining semantic strength of each marked word and obtaining a high-strength semantic word sequence; quantifying analysis contribution of each marked word at each time and initial analysis limitation at each time, and then obtaining buffer semantic integrity reservation degrees at each time; judging whether to establish a buffer space in real time, adjusting an analysis process, and finally outputting semantic weights of each word; screening a plurality of core words, combining the knowledge graph to obtain a relationship path, and then generating a reply text. The application aims to solve the problem that text key word analysis is disturbed by a word order, so that semantic weights cannot be accurately obtained.

The invention discloses a Tibetan rhythm structure prediction method based on grammar information, relates to the technical field of speech synthesis, and is applied to the field of rhythm structure prediction in Tibetan speech synthesis. The syntactic information-based Tibetan rhythm structure prediction method comprises the following steps of S1, completing Tibetan analysis and virtual word continuous quantization, extracting a grammar probability, a hierarchical focus and a pause difference, and storing and constructing a Tibetan rhythm prediction database after preprocessing; s2, based on the strength of the virtual word continuing relation, the influence range hit mark and the word order distance, boundary discrimination is carried out; s3, entropy feature analysis is carried out through the grammar role probability data and the boundary judgment data; s4, generating a three-layer rhythm boundary type sequence according to boundary forming, and inhibiting over-dense pause; and S5, carrying out rhythm intensity evaluation through the pause duration difference and the grammar information entropy data. The problems of pause, confusion and increased understanding burden caused by dislocation of rhythm and semantic levels in Tibetan long speech synthesis are solved.

The present application relates to speech processing technology, disclose a kind of intelligent speech recognition method, comprising: obtaining speech signal, the frame processing of speech signal is carried out, and the frame speech signal of obtaining is obtained;The characteristic parameter of the frame speech signal is analyzed, and the phoneme information parameter and the tone information parameter are obtained;According to the phoneme information parameter and the tone information parameter frame synchronization word recognition is carried out to the frame speech signal, and the frame word sequence of obtaining is obtained;Inquiry the starting end and the end of each sentence in the frame word sequence, the frame word sequence is split into the word sequence segment under each sentence;Word reorganization is carried out to the word sequence segment in turn using pre-constructed finite state machine, and the recognition sentence is generated.The present application also proposes a kind of intelligent speech recognition device, electronic equipment and storage medium.The present application can solve the problem that there is sequence disorder in speech recognition result.

The present application relates to the technical field of cloud collaboration, in particular to a network false information detection method and system based on large model fine-tuning, comprising the following steps: extracting mispositioned text and context information, judging semantic span to form an association set, separating factual sentences, emotional sentences and provenance sentences to track semantic chains, comparing original texts to identify offset segments, adjusting the order of analysis to analyze cause and effect, and obtaining false information determination conclusions. In the present application, by correlating and extracting the mispositioned relationship between semantic segments and tracking the chain, the logical order can be reconstructed at the sentence level, the semantic deviation caused by paragraph jumps can be eliminated, the content cause and effect connection and the context coherence can be maintained, the corresponding relationship between factual sentences, emotional tendencies and time elements can be identified simultaneously in semantic path analysis, the semantic judgment remains continuous and stable, the dynamic comparison and offset correction of the quoted content and the original context are completed in the multi-source corpus scene, and the stability and conclusion reliability of false information identification are improved.

This invention discloses an active perception scheme optimization system based on the Agent-based Tt e AI intelligent agent, relating to the field of voltage control technology. When a user inputs various consultation statements, this invention combines the Agent intelligent agent and historical consultation information to generate consultation statement recommendations corresponding to each input word. When the user does not select a recommended consultation statement, the system performs component analysis and word order correction on each input consultation statement, followed by semantic analysis and emotion classification. Finally, it integrates the semantic and emotion classifications of each consultation statement to match a personalized response to the user, generating a personalized consultation profile for subsequent auxiliary recommendations. This invention, based on Agent-based intelligent perception, makes interaction more efficient and proactive, significantly improving the accuracy of semantic recognition and the relevance of responses, thus achieving intelligent responses to user inquiries.

The invention relates to the technical field of nephropathy nursing training, and discloses a nephropathy nursing training management method and system based on a knowledge graph. The system comprises a training data preprocessing module, a term word order modeling module, a knowledge graph path generation module, a path cross analysis module, a semantic category judgment module and a training scheme generation module. The training data preprocessing module processes the multi-source nephropathy nursing training materials to obtain preprocessed data subsets; a term word order modeling module extracts a term text according to the term text, models the term text and generates a term word order sequence template; the knowledge graph path generation module generates a knowledge graph path structure according to the template; the path crossing analysis module analyzes the structure to obtain a path crossing set; the semantic category judgment module judges semantic categories based on the set and generates a semantic tag group; and the training scheme generation module is combined with the semantic tag group and the training evaluation model set to generate a personalized training scheme, so that the training process can be optimized, and the training pertinence and effectiveness can be improved.

The application provides a text clustering method, device and equipment and readable medium, the method comprises the following steps: establishing a vocabulary and calculating the word vector of each word in the vocabulary; obtaining the text vector of each text to be clustered and forming a text vector set, and calculating the distance between each two text vectors in the text vector set; randomly selecting a threshold number of text vectors in the text vector set as candidate center vectors, and dividing the text vectors into two categories by taking each two text vectors as a group and sequentially taking the candidate center vectors as center vectors; selecting the center vector with the maximum confusion degree in the center vector group with the minimum confusion degree in each division and the corresponding classified text vector, and repeating the previous step with the selected text vector until a preset condition is reached. By using the scheme of the application, efficient semantic clustering of short texts can be achieved, and automatic clustering of short texts can be achieved while fully preserving the semantic and sequence information of the texts.

The invention relates to the technical field of intelligent chatting, in particular to an intelligent chatting method and system based on a large model, and the method comprises the following steps: extracting verb combinations and target phrases, labeling semantic transition and fracture, generating coherent chain tags, measuring offset to judge derailment, aggregating stable chunks, aligning centroid lexical items, recognizing focus offset, and reconstructing low-efficiency sentence segments. And generating a control response text. According to the method, the topic content, separated from the current semantic path, in the response statement can be recognized by extracting the action verb combination, limiting the target word group and the logic connection mark item, constructing the structure sequence according to the word order and combining semantic unit lexical position matching and offset distance measurement in the focus transfer direction; statistics of sentence pattern templates with consistent semantic directions is carried out, role change or target replacement phrases are eliminated, sentence phrases with low scores are eliminated, and reconstruction and rewriting at a paragraph level are carried out, so that semantic coherence and topic consistency of response contents in multiple rounds of dialogues are improved.

The invention belongs to the technical field of computer information processing, and discloses a file similarity detection method based on Simhash fusion keyword and key sentence extraction, which comprises the following steps of: firstly, carrying out text preprocessing and dual-granularity feature extraction to respectively obtain keywords and key sentences with weights; secondly, providing a weight fusion strategy, and contributing keyword weights to key sentences according to the occurrence frequency to form fusion weights; secondly, optimized SimHash feature coding is carried out, 64-bit fingerprints are independently generated for keywords and key sentences by adopting grouping hash functions based on different seeds, and the 64-bit fingerprints and the key sentences are spliced into a 128-bit final feature fingerprint; and finally, the similarity is judged by calculating the Hamming distance between fingerprints of different files. According to the method, through word and sentence dual-granularity feature fusion and grouped Hash coding, the detection precision and robustness of an algorithm on synonymous replacement, word order adjustment and paragraph recombination are remarkably improved.

The application relates to a conference minutes generation method combining text optimization and semantic relationship analysis. First, the voice recognition component is used to transcribe the conference recording in real time, and the transcription result is processed again by combining a text optimization model to improve the consistency of the transcription text and the voice content. Then, the optimized text is subjected to deep semantic analysis to automatically identify key information such as conference theme, topic, decision and task content, and generate a structured conference minutes document under the constraint of a template engine to realize efficient arrangement and automatic archiving of conference content. The application has the advantages that the accuracy and professional adaptability of voice transcription are improved, the homonym confusion, proper noun recognition error and word order ambiguity problem are effectively solved, and high-quality, structured and semantically coherent conference minutes generation is realized.

The present invention relates to a classification apparatus and method capable of classifying a document by considering all of meaning, context, and order information of each word present within the document. The classification apparatus comprises: a data collection module that collects document data; a context extraction module that generates a context channel on the basis of the document data, and generates a context feature map vector on the basis of the generated context channel and a CNN model; a sequence extraction module that generates a sequence channel on the basis of the document data, and generates a sequence feature map vector on the basis of the generated sequence channel and a transformer model; and a combination module that generates a classification result for the document data on the basis of the context feature map vector and the sequence feature map vector, wherein the context feature map vector includes having vectorized semantic information of each word included in the document data by reflecting context of the document data.

To provide a teaching material for facilitating composition in a language to be learned.SOLUTION: To provide a language teaching material set which enables a user to easily learn grammar by using composition cards on which composition terms of a learning object language and headings of speaker language concepts as superordinate concepts of the composition terms are described and arranging the composition cards corresponding to the headings of the speaker language concepts arranged in the word order of the learning object language on a composition sheet.SELECTED DRAWING: Figure 9