34 results about "Word meaning" patented technology

The invention belongs to the technical field of ancient book digital processing, and discloses a training knowledge-fused ancient book word sense disambiguation deep learning method, which comprises the following steps of: constructing a training knowledge graph; preprocessing ancient book image texts; constructing a deep semantic disambiguation model fusing training knowledge; word sense disambiguation reasoning and result output; and system integration and intelligent application interface design. The high-precision word sense disambiguation method oriented to ancient books and texts is constructed by fusing training knowledge and a deep learning technology, so that the recognition capability of complex semantic phenomena such as polysemy words, ancient and modern heterosemy and common and false characters is remarkably improved, the interpretability and field adaptability of the model are enhanced, the dependence on manual training is reduced, and the training efficiency is improved. The method realizes efficient understanding and intelligent processing of the semantics of the ancient books under the condition of low resources, and has good popularization and application prospects and culture inheritance value.

The application provides a content semantic-based fusion detection method and system, which performs syntactic analysis and semantic analysis through a syntactic model and a semantic analysis model to perform fusion detection, the syntactic model completes positioning of an object according to a subject and a predicate to obtain a triple topic vector, and then outputs word meanings through the semantic analysis model to reorganize a new sentence, and whether the sentence contains non-compliant content is detected, thereby overcoming the problem that the prior art cannot detect incomplete sentence structures.

This invention provides a method and apparatus for generating vocabulary memorization content, relating to the field of data processing technology. The method includes: acquiring multidimensional feature data of a target word to be memorized and a target user; wherein the multidimensional feature data includes at least the target user's personalized image features and learning stage identifier; constructing a target scene corpus based on the semantic information of the target word to be memorized; constructing structured prompt information for driving a generative model based on the target word to be memorized, the target scene corpus, and the multidimensional feature data; inputting the structured prompt information into a pre-trained generative artificial intelligence model to generate a dynamic mnemonic comic, and displaying the dynamic mnemonic comic on a terminal device; wherein the dynamic mnemonic comic includes a character image generated based on the personalized image features, and the character image interprets the meaning of the target word to be memorized within the context defined by the target scene corpus.

This invention provides a method for calculating semantic similarity based on adjacent word features, relating to the field of natural language processing. First, in the example word extraction module, example words are extracted from the example sentence and the sentence to be matched using the longest common substring algorithm. Second, in the example sentence processing module, word segmentation is performed on the example sentence excluding the example words, and part-of-speech tagging is applied to the example words. Then, in the feature extraction module, features are extracted from the words surrounding the example words in the example sentence. The correlation between the surrounding words and the example words and option words is calculated using a corpus. The calculated results are weighted and combined with their respective features to form the features of the example words and option words. Finally, in the option processing module, the part-of-speech of the option words is compared with that of the example words, and a similarity score is calculated based on the features of the option words to select the optimal option word. This invention determines the features of the current word by extracting features from surrounding words and calculating correlation; it uses a corpus to calculate the mutual information between two words to achieve the correlation between the two words.

The invention discloses a lyric processing method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a lyric text of a first song; vocabulary information of the first song is obtained according to the word bank and the lyric text of the first song, and the vocabulary information comprises at least one target vocabulary and a lyric statement corresponding to the at least one target vocabulary; according to a first target vocabulary in the at least one target vocabulary, a lyric statement corresponding to the first target vocabulary, the identification information of the first song and metadata of the first target vocabulary, obtaining a packaging object corresponding to the first target vocabulary; and generating translation information of the first target vocabulary through the generative language model according to the packaging object corresponding to the first target vocabulary. According to the method and the device, high-accuracy word sense disambiguation of the target vocabulary is realized, the consistency of the finally determined target semantics and the current semantic environment is ensured, and the accuracy of semantic understanding is improved.

The application provides a word sense disambiguation method and device, wherein the method comprises the following steps: determining a to-be-disambiguated entity existing in to-be-disambiguated text and a corresponding candidate entity list based on an RPA knowledge graph; determining embedding features corresponding to each candidate entity in the candidate entity list and the to-be-disambiguated entity through RPA feature extraction based on each candidate entity and the to-be-disambiguated entity; and determining whether the to-be-disambiguated entity and the candidate entity are the same entity based on a word sense disambiguation model, the embedding features corresponding to each candidate entity, and the embedding features corresponding to the to-be-disambiguated entity. The application realizes the comparison of embedding features of the to-be-disambiguated text and the candidate text by comprehensively embedding entity features, entity context features and word features, determines whether the to-be-disambiguated entity and the candidate entity are the same entity, and obtains more rich and comprehensive text information, which is conducive to accurately analyzing the word sense and improving the word sense disambiguation accuracy.

The application discloses a relation extraction method fusing entity type representation and relation representation, and belongs to the technical field of relation extraction. The application relates to a text-subject-object weakly related semantic representation mechanism, entity type information is introduced to replace entity word meaning information, and then the dependence of an extraction model on subject-object semantic association is reduced; on the basis of the above, the application further models abstract semantic information of entity relation, and is fused with context semantic representation containing subject-object type information to generate semantic mapping of the entity relation, so that more accurate prediction effect of the subject-relation-object triple is obtained.

The application discloses a similarity discrimination method and system for new word sense primitive recommendation, and comprises the following steps: in the HowNet word set, similar words to a new word are selected by a similarity discrimination model to form a similar word set; a local "word-semantic item-semantic primitive" relationship network is constructed according to all the words in the similar word set, the corresponding concept semantic items and semantic primitives of the words; semantic primitive nodes are selected based on a network node importance sorting method, a recommendation index of the semantic primitive nodes is generated according to the standardization degree centrality and the intermediate centrality of the semantic primitive nodes, the importance of the semantic primitive nodes is evaluated, the correlation between an unregistered word and a semantic primitive is established by taking the similar word set as a bridge, the sorting and selection of the candidate semantic primitives of the unregistered word are completed through the recommendation index, and the HowNet is expanded through the new word; the application effectively solves the similarity discrimination problem between the unregistered word and the word table word, and effectively solves the selection problem of the candidate semantic primitives.

The application discloses a kind of based on knowledge graph's vocabulary data modeling method, specifically includes: collecting vocabulary text, pronunciation data, scene corpus and interactive record, form original vocabulary data;To original vocabulary data executes word item segmentation, word meaning classification, scene marking and cognitive label extraction, form standardization vocabulary unit;Based on standardization vocabulary unit constructs language-cognition-context triadic knowledge graph;Based on triadic knowledge graph extraction language ability subgraph, form ability coupling matrix;Based on ability coupling matrix forms hierarchical ability interval set;Based on hierarchical ability interval set forms vocabulary modeling sequence;Based on vocabulary modeling sequence output vocabulary graph model.The application realizes the unified arrangement of vocabulary text, pronunciation data, scene corpus and interactive record, realizes the associated modeling of language attribute, cognitive state and context information, realizes vocabulary graph model output.

This system provides an information processing system capable of outputting response data that aligns with the user's motivations or values. [Solution] The information processing system obtains content containing expressions representing reasons, intentions, backgrounds, or objectives from construction data as reason sentences, converts the obtained reason sentences into multidimensional embedding vectors using an embedding model to obtain reason vectors, generates reason data by associating the obtained reason sentences with the reason vectors, stores the generated reason data to construct a reason corpus, obtains content containing expressions representing reasons, intentions, backgrounds, or objectives from user query data as query reason sentences, converts the obtained query reason sentences into multidimensional embedding vectors using an embedding model to obtain query vectors, calculates the similarity between the query vectors and the reason vectors included in the reason corpus, and searches a predetermined number of reason data from the reason corpus based on the calculated similarity.

This invention proposes an intelligent enterprise accounting voucher generation and compliance review system, comprising: a text processing module, including a target lexicon generation unit, which can extract several negative words, several modifier texts paired with each negative word, and several correction words from preprocessed enterprise accounting text data and generate a target lexicon; and a current text recognition module, which is used to calculate the part-of-speech relevance C of the current text based on the target lexicon, and to identify the current text as a negative word or a corrected word based on the part-of-speech relevance C and the context of the current text, so as to perform a correction operation based on the recognition result, thereby solving the problem that the existing system has insufficient ability to analyze the scope of negative words and the consistency of context before and after correction, which leads to the existing system easily misclassifying negative statements and ignoring the associated impact of correction operations, thus causing voucher generation errors or compliance review loopholes.

The invention relates to a polysemy content display method and device for electronic learning equipment and electronic equipment, and the method comprises the steps: building and storing a mapping relation between at least two word meaning items of a target word and corresponding example sentences, and enabling each word meaning item to be uniquely associated with at least one corresponding example sentence; a selectable list containing at least two word meaning items is displayed in a first area of a display interface of the electronic paper display screen, and the list is provided with a currently activated visual focus indication; displaying example sentences corresponding to the word meaning items identified by the visual focus indication at present in a second area of the display interface of the electronic paper display screen; in response to a received selection instruction for a target word meaning item in the selectable list, switching the visual focus indication to the target word meaning item; in response to the switching indicated by the visual focus, the content displayed by the second area is synchronously and uniquely updated. According to the method, the cognitive burden of the user among different display contents is eliminated, and the concentration degree, the understanding accuracy and the operation efficiency of word learning are improved.

The application provides a method for adjusting robot tone, role and speech speed based on natural language mode, comprising the following steps: S1, confirming a first call event and confirming a calling role, and defining a tone from a tone data sub-library according to the calling role; S2, calling a phone; S3, confirming first text information according to the first call event, and disassembling the first text information according to word meaning, and then audioizing the disassembled first text information to form gap output type first voice information; S4, binding the first voice information with an emotional state in an emotional data sub-library, and adding a mood adverbial segment type to the first voice information according to the emotional state to form second voice information; S5, segment type broadcasting the second voice information, and judging whether feedback information is received in real time; if yes, adjusting the first call event according to the feedback information to form a second call event and re-executing steps S3-S5.

The keyword matching method, device and electronic equipment across language environments provided by the embodiments of the present application relate to the technical field of information retrieval. First, a source language keyword used for matching a target language text is obtained; then, the source language keyword is processed by word segmentation, and the source language keyword is classified into a short keyword string or a long keyword string; then, when the source language keyword is a short keyword string, keyword cross-language matching is performed through semantic expansion to optimize the missing report problem of the source language keyword exact matching; when the language keyword is a long keyword string, the source language keyword cross-language matching is performed based on a semantic-level fuzzy matching technology, the overall matching degree of the keyword is calculated in combination with the semantic matching value and the overall relevance of the matching segment to the target text, so as to comprehensively consider the overall matching degree and the local matching degree of the source language keyword. The above scheme can adopt different matching strategies based on the classification of the source language keyword, and ensure the accuracy of the matching result.

Embodiments of the present application provide a kind of allegory sentence pattern component extraction method, device, computer readable medium and equipment.The method comprises: determining the part-of-speech result, part-of-speech mask matrix and adjacent matrix based on syntax dependency relationship corresponding to the text to be processed;Word meaning retrieval is carried out to the part-of-speech of noun, and the noun interpretation set corresponding to the text to be processed is obtained;The text to be processed and the noun interpretation set are spliced, and input into BERT encoder, and the corresponding text representation matrix is obtained;The text representation matrix is multiplied by the part-of-speech mask matrix, and the corresponding word node matrix is obtained;Based on GAT algorithm, the word node matrix and the adjacent matrix are updated to obtain the node representation corresponding to the text to be processed;Based on the node representation, the allegory component extraction of the text to be processed is carried out.The technical scheme of the embodiment of the present application improves the accuracy of component identification and extraction in allegory sentence pattern.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a transfer transaction failure compensation method and device, and the method comprises the steps: responding to a transfer transaction failure record, obtaining a transaction log, extracting a transaction feature vector according to the transaction log, inputting the transaction feature vector into a first classification model, and obtaining a first transaction failure type; if the first transaction failure type belongs to the preset classification set, processing according to a preset compensation mechanism; if not, using the word vector model to perform word meaning feature vector extraction and inputting the word meaning feature vector into a second classification model to obtain a second transaction failure type; if the second transaction failure type belongs to the preset classification set, processing according to a preset compensation mechanism; and if not, generating error information and sending the error information to operation and maintenance personnel. Through the embodiment of the invention, the transaction failure type identification accuracy can be improved, so that the transfer failure processing efficiency is improved, and the manual intervention cost is reduced.

The invention relates to a method for quickly retrieving key contents of a paper document, which comprises the following steps of: realizing electronic conversion of the paper document by utilizing an OCR (Optical Character Recognition) technology, and realizing word frequency statistics, word meaning analysis, document theme analysis and table theme analysis of the electronic document through an offline deployed pre-training model to obtain a keyword list and a theme list; according to the method, the key content and the key theme are combined, a keyword-theme-page number corresponding relation is generated, a two-dimensional table is finally generated, the key content serves as the column of the table, the key theme serves as the row of the table, the page number is recorded on the intersection point of the row and the column, and a reader can quickly position the key content of the article and the page number where the corresponding theme is located by checking the two-dimensional table. According to the method, a set of document key content lookup table uniformly generated by software is designed, the document key content lookup table formed by one page or multiple pages is newly added between the catalog and the text of the paper document, and a reader knows the key content and key theme of the document through the table, so that the document key content and the key theme can be obtained. And the interested contents and the corresponding themes are quickly positioned in a table look-up manner, so that the document content retrieval efficiency is improved.

The embodiment of the invention provides a word vector text similarity analysis method and system in a recruitment field, and can solve the problems that in a core link of resume and post matching, a model has significant deviation in understanding of skills and post requirements of job seekers, so that the recommendation precision is reduced, and the misjudgment rate is increased. The method comprises the steps of obtaining a technical word set from a recruitment field corpus based on a pre-trained named entity recognition model; inputting the high-frequency technical keyword set into the large language model, performing category judgment on each technical keyword under the constraint of a technical keyword classification framework used for describing technical knowledge in the recruitment field, selecting a corresponding multi-round question prompt word template based on the category to which each technical keyword belongs, and performing multi-round multi-angle question on the large language model, and training a word meaning matching model on the basis of the generated refined training corpus used for representing the technical semantic relationship in the recruitment field, so as to obtain a plurality of question and answer fragments and context samples generated around each technical keyword.

The invention discloses a GraphRAG-based ancient Chinese language knowledge question-answering system construction method, and relates to the technical field of ancient Chinese language knowledge question-answering system construction, and the method comprises the following steps: constructing an ancient Chinese language knowledge question-answering system, collecting the age, education stage and region of a user, obtaining the basic information of the user, and collecting questions proposed by the user, obtaining user question related data; performing user division processing according to the age, the education stage and the region of the user; carrying out answer sorting processing on the questions proposed by the user to obtain answer sorting information; generating answers to the questions proposed by the user, and answering the questions; the method is used for solving the problems that according to an existing ancient Chinese knowledge question-answering system construction technology, when an ancient Chinese word meaning item problem is answered through a constructed system, the answered meaning items cannot be dynamically sorted according to basic information of a user and historical problems of the user, the meaning items needed by the user are preferentially arranged, and the user experience is poor. And the use experience and question answering efficiency of the user are improved.

The invention relates to the technical field of customer service intelligent response, in particular to a customer service intelligent response method based on deep learning and knowledge migration, which comprises the following steps: acquiring user statements and contexts to construct semantic expressions, tracking direction changes to generate a context atlas, extracting frequent paths and overlapped phrases to generate response indexes, and analyzing direction offset to screen migration conflict points. And recombining the sentence patterns to generate coherent migration response content. According to the method, by constructing a semantic sequence with channel identifiers and a time sequence, ordered organization of multi-dimensional semantic information, tracking of a direction change track between channels, recognition of a semantic path structure overlapping region, extraction of core phrases based on frequent steering and positioning of potential conflict contents in combination with an included angle relationship between a main path and an offset path are realized; the adaptability and coverage range of focus semantics are enhanced through word meaning replacement and sentence pattern reconstruction, and the semantic stability and pertinence of response content in a complex interaction scene are improved.

The application discloses a kind of processing method, equipment, medium and product of business vocabulary, it is related to data processing technical field, the method comprises: the associated vocabulary of determining the vocabulary to be processed;Determine the semantic vector of the vocabulary to be processed and the semantic vector of associated vocabulary, and according to the semantic vector of the vocabulary to be processed and the semantic vector of associated vocabulary determine the word meaning diagnostic result of associated vocabulary and the vocabulary to be processed;When word meaning diagnostic result meets vocabulary adjustment condition, according to word meaning diagnostic result, the semantic information of the vocabulary to be processed and the semantic information of associated vocabulary, determine target vocabulary from the vocabulary to be processed and associated vocabulary, and adjust the vocabulary to be processed and associated vocabulary based on target vocabulary.The application can effectively improve the "multiple words one meaning" phenomenon of business node vocabulary, improve the vocabulary standardization and uniformity of financial information system, reduce data maintenance cost, the work difficulty and workload of technical personnel.

The invention discloses a construction method and device of an ancient Chinese calendar word meaning corpus and a storage medium. According to the method, firstly, ancient Chinese corpora containing time information are collected, then, a pre-trained language model is used for carrying out word meaning automatic labeling on words in the preprocessed ancient Chinese corpora, and word meaning alignment is carried out on the words subjected to automatic labeling. And finally, summarizing annotation alignment results, and carrying out statistics on word meaning duration proportion information to obtain an ancient Chinese duration word meaning corpus. According to the historical word meaning corpus of the ancient Chinese constructed by the method, functions of semantic item-level corpus retrieval, word semantic evolution analysis, word meaning clustering analysis and the like can be realized, so that the rule and the mechanism of the semantic change of the Chinese vocabularies can be disclosed, and important data resources are provided for the fields of classical Chinese teaching, dictionary compilation and related humanity research; and the teaching and research efficiency is greatly improved.

Aiming at the defects of an existing test method in the field of word sense disambiguation test of machine translation in the aspects of triggering effective word sense conversion, maintaining original sense and maintaining semantic consistency, the invention provides a machine translation polysemous word translation evaluation method based on semantic item trigger word replacement. The method comprises the following steps: firstly, constructing a polysemy semantic item library, and screening source language sentences containing polysemy from a parallel corpus according to the semantic item library; performing natural language processing on the sentence, and identifying a trigger word of a polysemy special definition item in the sentence; replacing the trigger word with a replacement word to generate a variant sentence; performing machine translation and alignment on the original sentences and the variant sentences to obtain expressions of polysemy words in translations; and calculating the similarity of the polysemy translation expression, and judging whether a polysemy disambiguation error exists or not. Through a trigger word replacement strategy, a semantic controlled contrast sentence pair can be constructed, potential errors of a system in polysemous word translation are effectively revealed, and the pertinence and effectiveness of evaluation are improved.

The application relates to the technical field of language translation, and particularly discloses a multilingual real-time translation cash register terminal system, which comprises an interactive language recognition module, a translation and ambiguity content recognition module, and an alternative translation screening module. The interactive language recognition module judges the interactive language of a customer based on the first interactive voice data input by the customer. The translation and ambiguity content recognition module translates the interactive voice data input by the customer or a cashier in real time and determines all ambiguous words, all ambiguous sentences and all alternative translation results in the corresponding interactive voice data based on the interactive language of the customer or the interactive language of the cashier. The alternative translation screening module screens the final translation result of the real-time input interactive voice data from all the alternative translation results based on the adjacent context of all the ambiguous words, the adjacent context of all the ambiguous sentences and a preset cash register scenario multi-word meaning weight information base. The application improves the communication efficiency and accuracy in the cash register process and provides more convenient and smooth service experience for the customer and the cashier.

The invention discloses an acute respiratory disease classification method based on a dictionary, and relates to the technical field of medical information processing, and the method comprises the following steps: building a cross-time-sequence semantic audit baseline, collecting synonym events in a medical text stream through employing a nanosecond-level unified time scale, generating a semantic oscillation spectrum and a weight response curve, and constructing a dynamic feature mapping model; and based on the semantic oscillation spectrum and the weight response curve, constructing a synonym coherence recognition engine, recognizing a semantic oscillation core region and a critical semantic window through a phase comb index and context mutual identification mechanism, and extracting a leading index of weight drift. According to the method, through time sequence semantic collection, oscillation recognition, causal playback and word meaning deformation, a steady-state anchor point and variation control mechanism is constructed, self-sensing, self-adjusting and self-repairing of a classification model to semantic disturbance are achieved, and the stability and accuracy of medical text classification are remarkably improved.

The invention provides a traditional Chinese medicine cross-language aided translation system based on artificial intelligence, and belongs to the technical field of traditional Chinese medicine translation. The traditional Chinese medicine cross-language aided translation system based on artificial intelligence comprises a data acquisition module, a semantic recognition module, a semantic correction module, a translation strategy output module and a translation evaluation module. The system can track semantic distance changes between term translation and historical standards in real time, a translation migration file is established for recognized deviations, the corresponding relation between word meaning drift paths and contexts is recorded, the system can judge whether the deviations belong to common error types or not, rapid error correction is conducted on the basis of a term evolution database, and therefore the accuracy of term translation is improved. The mechanism does not depend on a fixed dictionary rule, but performs combined judgment according to factors such as a semantic change trend, a context structure adaptation degree and the frequency of historical deviation behaviors, so that the intelligence and the adaptability of deviation judgment are greatly improved, and the self-correction capability and the fault tolerance of a translation system are remarkably improved.

The application provides a natural language text data intelligent classification method and system based on deep learning, and relates to the technical field of natural language processing.The method comprises the following steps: in step 1, the real semantics of a target word is analyzed by using a context perception mechanism in view of an adversarial variant existing in the text, a pre-training process is combined with a word meaning library and a dynamic learning rate adjustment strategy, and a set of candidate replacement words with consistent semantics is generated; in step 2, multi-dimensional semantic similarity calculation and sentiment tendency discrimination are carried out based on the set of candidate replacement words, a suitable word conforming to the original cultural background is determined through a context adaptation strategy, and a standardized text sequence is generated. Through multi-dimensional semantic analysis, cultural context fusion, cross-granularity feature construction and dynamic parameter correction, the accuracy and adaptability of natural language text classification are realized.

The application discloses a kind of text error correction method, system and storage medium, and the application belongs to text error correction technical field.The present application comprises the following steps: obtaining confirmed error-free text data as training corpus, and adding segmentation mark by artificial marking, word pair segmentation is carried out on sample, and the word pair and its part of speech of each text are obtained.Then, the word pair segmentation model is trained using these data, so that it can output the segmented word pair and the corresponding part of speech.After inputting the text to be corrected into the model, the semantic consistency index is evaluated.Considering the word length and the part of speech, the complexity index of the word pair is calculated, and the error judgment coefficient is generated by combining the two items.By comparing with the set threshold, the word pair to be corrected is marked out.The word meaning similarity of the word pair to be corrected and the word pair in the candidate word pair library is calculated, and the word pair with the highest similarity is selected to replace to realize text error correction.This method effectively integrates word pair analysis and similarity calculation to improve the accuracy and fluency of the text.

The present application relates to a kind of Chinese word sense disambiguation method of combining multi-channel hybrid dilated convolution neural network with residual and attention (Combining Multi-Channel Hybrid Dilated Convolution Neural Network with Residual and Attention, MHDCNN-RA). The present application first carries out word segmentation, part-of-speech tagging, semantic class labeling to Chinese sentence containing ambiguous words, obtains processed training corpus and test corpus. Then, the word sense disambiguation model is trained using the training corpus, and the optimized MHDCNN-RA model is obtained. On the optimized MHDCNN-RA model, the test corpus is disambiguated, and the weight of the ambiguous word under each semantic category is obtained. The semantic category with the maximum weight is the semantic category of the ambiguous word. The present application realizes good disambiguation of ambiguous words and more accurately judges the true meaning of ambiguous words.

The application discloses a kind of sample augmentation methods and systems for power grid regulation and control large model training, belong to power grid dispatching automation technical field.The method includes: obtaining the power grid regulation and control text to be augmented, according to the preset power field special dictionary, synonym replacement operation is carried out to the power grid regulation and control text to be augmented;The comprehensive importance score of each vocabulary in the power grid regulation and control text to be augmented is calculated, and the random deletion operation under semantic fidelity is carried out to the power grid regulation and control text to be augmented based on the comprehensive importance score;Protective vocabulary in the power grid regulation and control text to be augmented is identified, and the random insertion operation under the meaning preservation of the power grid regulation and control text to be augmented is carried out;Synonym replacement operation, random deletion operation under semantic fidelity and random insertion operation under the meaning preservation of the power grid regulation and control text to be augmented are cooperatively executed, and the augmented power grid regulation and control text sample is generated.The application solves the problems of semantic distortion, physical meaning loss and other problems in the application of general augmentation method in power grid regulation field.