56 results about "Word meaning" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an English teaching training system and method fusing semantic matching and cognitive assessment, and the method comprises the steps: synchronously capturing a text response, a voice intonation, an eye movement track, a facial micro-expression and a touch rhythm generated in a learning process; semantic deviation deconstruction and cognitive intention quantization processing are carried out on the learning interaction original sequence, and a bidirectional deep semantic matching network is adopted to carry out context alignment on student answers and target corpora; based on the word meaning divergence point set and the cognitive load multi-scale vector, extracting a nonlinear diffusion trajectory of a learning state by using a time gating multi-layer recursive trajectory evolution algorithm; the knowledge point nodes, the deviation type nodes and the emotion triggering nodes associated with the emotion instability candidate segments are fused to construct a local learning map; and forming an emotion cognition feedback result driven by learning interest based on the local learning map and the self-adaptive error correction intervention sequence. The method has the advantage of improving the learning interest of students.

The invention 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, and the method comprises the steps: 1, employing a context awareness mechanism to analyze the real semantics of a target vocabulary according to an antagonistic variant existing in a text, and obtaining a target vocabulary; in combination with a word meaning library and a pre-training process of a dynamic learning rate adjustment strategy, generating a candidate replacement vocabulary set with consistent semantics; and step 2, based on the candidate replacement vocabulary set, performing multi-dimensional semantic similarity calculation and emotional tendency discrimination, determining applicable vocabularies conforming to an original culture background through a context adaptation strategy, and generating a standardized text sequence. According to the method, 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 invention discloses a personalized semantic understanding system for assisting an autonomous mobile platform, and the system comprises a user personalized word meaning library which is used for recording the mapping relation between the user habit expression and the corresponding operation; the fuzzy semantic analysis module is used for identifying fuzzy expression in a natural language instruction of a user and generating a plurality of candidate targets or operation options of semantic generalization; the multi-round context modeling module is used for establishing a semantic context based on session history and performing disambiguation and complementation on current semantics; and the intention generation module is used for converting the processed semantic information into a structured instruction. The personalized semantic understanding system is reasonable in structural design, all the modules cooperate with one another, the effect that 1 + 1 is larger than 2 can be achieved, fuzzy semantic understanding and multi-round context modeling can be conducted, the personalized requirement of a user can be met, semantic understanding precision is high, user adaptability is good, and the personalized semantic understanding system is suitable for man-machine interaction of an autonomous mobile platform.

The invention provides a word meaning sentence construction method and system based on speech thinking, and relates to the technical field of natural language processing, and the method comprises the steps: 1, obtaining an initial statement input by a user, a preset rhetorical manipulation type and literature style preference, and analyzing a semantic core and an emotional tendency in the initial statement, based on the semantic core, calling a rhetorical knowledge base of a Uygur's speech thinking theory, including a metaphor mapping matrix, a symbol association network and a grammar deformation rule set, and matching an adaptive composite rhetorical structure; step 2, constructing a three-dimensional situation semantic field based on the semantic core and the emotional tendency, and extracting and acquiring initial weight parameters of each dimension of an author cognition dimension, a reader acceptance dimension and a text function dimension through multi-modal features; according to the method, word meaning sentence construction is performed in combination with a speech thinking theory, so that image coordination and language adaptation of candidate sentence patterns are realized, and language expression is improved.

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.

Systems and methods evaluate the meanings of whole words bound within the meanings of their parts by analyzing and identifying features and components of words (such as letters and syllables), using sound information, for example phoneme class structure, to find sound correlation and using synonymity to find meaning correlation between words (or word parts) and a data set of known / identifiable sound / meaning associations called morphemes to find morpheme‑word‑parts that optimally describe an input word. Various means are described of compiling morphemes into a data set. The systems and methods further include analyzing the semantic functions of word parts and their modifying interrelationships, and using identifications made by one or more analyses. The systems and methods can evaluate or improve understanding of word meanings. The systems and methods are useful in researching sound / meaning associations in language generally and for improving language processing generally by the incorporation of more semantic information.

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 present invention provides a method and system for processing local information context-related word components. The method inputs a sentence into a syntactic model, sets extraction windows of different widths for sentence segmentation, obtains a first word component, determines the length of the context content according to the extraction window, performs association calculations with the previous and next sentences respectively, determines the context content, performs semantic analysis on the first word component, and uses its context content as a reference input for semantic analysis to derive the word meaning corresponding to the first word component. By predicting the word meaning of the context content, the word meaning corresponding to the first word component can be corrected.

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 present invention discloses a public opinion detection and analysis system thereof, which relates to the technical field of public opinion detection and analysis, and includes: a word sense association module, which is used to construct a word sense association tree of characters, wherein the root node is any character, the child nodes are characters related to the root node, and the child nodes are assigned values ​​according to the co-occurrence frequency, so that the sum of the values ​​of all child nodes is one; a semantic association module, which is used to construct a word sense association tree of each child node based on the child node of the word sense association tree as the root node, until a preset number of layers is reached to obtain a semantic association tree; a keyword detection module, which is used to match and analyze the semantic association trees of the document to be analyzed and the keyword, and obtain a set of public opinion keywords for the document to be analyzed. By constructing a word association tree, a co-occurrence frequency matrix and a multi-level similarity matching calculation model, high accuracy, dynamic adaptability and high robustness of keyword recognition are achieved, and the accuracy and stability of keyword recognition in complex contexts are 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 invention relates to a character recognition method and system, and the method comprises the steps: inputting a target image comprising a to-be-recognized statement into a trained character recognition model, and obtaining an initial recognition result; the to-be-recognized statement comprises a plurality of to-be-recognized characters; the initial recognition result comprises a result character with the highest result probability corresponding to each character to be recognized; inputting the plurality of result characters into a trained context word meaning scoring model to obtain a word meaning score of each result character; and obtaining a target result character of each to-be-recognized character according to the word meaning score and the result probability. According to the method, the target result character of the to-be-recognized character is obtained by combining character recognition and context semantic scoring, and character recognition of the statement is performed by integrating the character recognition dimension and the semantic analysis dimension, so that the accuracy of character recognition can be improved.

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 present invention discloses a method and system for self-supervised word meaning understanding in Chinese, which belongs to the field of natural language processing technology. Through generative self-supervision and discriminative self-supervision, the accuracy of the model in identifying the specific meaning of words is improved. The generative method is to design pre-training tasks for word meaning understanding to improve the model's ability to represent ambiguous words in the text; the discriminative method is to construct pseudo-ambiguous word labeling data to guide the model to learn the ability to distinguish the differences between word meanings. Specifically, it includes three modules: model pre-training for word meaning understanding, construction of pseudo-ambiguous word labeling data, and establishment of a word meaning understanding model.

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 belongs to the technical field of document identification, and particularly relates to an intelligent document identification method and computer equipment. When a body text is processed, after a text detection model is used for recognizing the content of the body text, the step of semantic disambiguation processing is added, specifically, polysemy words and / or proper nouns in the content of the body text are / is recognized firstly, then the most possible word meanings of the polysemy words and / or proper nouns are determined through a parameter estimation method, and the word meanings of the polysemy words and / or proper nouns are determined. Further determining the word meaning of the polysemous word and / or the proper noun by using the trained neural network after a better result cannot be matched by the parameter estimation method (that is, the probability, estimated by the parameter estimation method, of the polysemous word and / or the proper noun belonging to the most probable word meaning is lower than a set probability threshold value); according to the method, the understanding deviation of polysemy words in different contexts is eliminated, the clear meaning of proper nouns is clear, analysis and extraction of key information of the document are achieved, a user can understand the obtained knowledge conveniently, and the document recognition effect is improved.

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 relates to the technical field of information retrieval, in particular to a Chinese language and literature database online query reading method and system, which comprises the following steps: extracting a word meaning item, a literature reference, a meaning length and a boundary mark, screening a candidate set, analyzing a subject pronoun and a verb collocation, positioning a paragraph and marking a word meaning item position, extracting a phrase structure comparison version offset, marking a keyword meaning number and a position, outputting a meaning, a source and a semantic path, and generating a comparison prompt; in the application, the positioning, display and structure alignment of the literature are optimized through semantic mapping, the accuracy and consistency of the target literature are improved, relevant content is accurately screened in combination with context information and version comparison, the structure offset between versions is automatically adjusted, the dependence on manual keyword setting is reduced, meanwhile, the generated word meaning comparison prompt group is optimized to improve the literature retrieval experience, the user can more quickly and accurately understand the word meaning and context relationship, and the information acquisition precision and efficiency are improved.

The invention relates to the technical field of information retrieval, in particular to an online query reading method and system for a Chinese language and literature database, which comprises the following steps: extracting word meaning items, quoting literatures, labeling paraphrase lengths and boundaries, screening candidate sets, analyzing the collocation of subject pronouns and verbs, positioning paragraphs and labeling the positions of the word meaning items, extracting phrase structures and comparing version offsets. According to the method, the positioning, the display and the structure alignment of the literature are optimized through semantic mapping, the accuracy and the consistency of the target literature are improved, and related contents are accurately screened in combination with comparison of context information and versions, so that the accuracy and the consistency of the target literature are improved. Compared with the prior art, the method has the advantages that the method is simple and easy to operate, structural deviation between versions is automatically adjusted, dependence on manual keyword setting is reduced, and meanwhile, the generated word meaning comparison prompt chunk optimizes document retrieval experience, so that a user can more quickly and accurately understand vocabulary meanings and contextual relationships, and information acquisition precision and efficiency 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.