86 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 discloses a sensitive word auditing method, which belongs to the field of auditing, and comprises the following steps of: firstly, preprocessing an input text, such as removing spaces, symbols, stop words and the like, so as to generate a word segmentation result; thirdly, constructing a dynamic synonym and a deformation word library, combining user behaviors and NLP technology updating, and expanding word meanings by applying a word vector model Word2Vec; then, based on BERT, a text context is analyzed, and metaphors and biguan languages are detected. Performing sensitive word primary screening and secondary screening by using a static word bank and an SVM (Support Vector Machine), and analyzing complex semantics by LSTM (Long Short Term Memory); the model is optimized based on a feedback self-learning mechanism, difficult contents are manually checked, an analysis report is generated, and finally a word bank and an algorithm are periodically updated to adapt to hotspots. The method has the beneficial effects that sensitive word auditing is improved through a self-learning mechanism, synonyms and deformed word banks can be dynamically updated, and private expressions are identified. BERT and LSTM model multi-level auditing are adopted, secondary screening is carried out in combination with SVM, and recognition of complex contexts and biguan languages is improved.

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 invention discloses a public opinion detection and analysis system, and relates to the technical field of public opinion detection and analysis, and the system comprises a word-meaning connection module which is used for constructing a word-meaning connection tree of words, a root node is any word, child nodes are words related to the root node, values are assigned to the child nodes according to a co-occurrence frequency, and the sum of the values of all the child nodes is one; the semantic connection module is used for constructing a word meaning connection tree of each child node by taking the child node of the word meaning connection tree as a root node until a preset layer number is reached, so as to obtain a semantic connection tree; and the keyword detection module is used for performing matching analysis on the to-be-analyzed document and the semantic linkage tree of the keywords to obtain a public opinion keyword set of the to-be-analyzed document. 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 keyword recognition precision and stability in a complex context are improved.

The present invention provides a method, device, electronic device and storage medium for constructing parallel sentence pairs, wherein the method comprises: obtaining a first sentence and a second sentence, wherein the first sentence and the second sentence correspond to different languages; determining a first semantic feature of the first sentence and a second semantic feature of the second sentence based on a cross-language language model, wherein the cross-language language model is obtained by training based on a semantic relationship between each participle in a first sample sentence and each participle in a second sample sentence, wherein the first sample sentence and the second sample sentence correspond to different languages; constructing parallel sentence pairs based on a similarity between the first semantic feature and the second semantic feature, wherein in an embodiment of the present invention, model training is performed using the semantic relationship between the participles contained in sample sentences of different languages, so that the performance of the trained model can be improved, and when facing a scarce resource language, the accuracy of sentence embedding is improved, the construction process of parallel sentence pairs is refined, and the quality of parallel sentence pair construction is improved.

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 invention relates to an Internet of Things equipment fingerprint generation method and system, and the method comprises the steps: collecting the protocol slogan data, with a label, of Internet of Things equipment through a network scanning tool, and decomposing the protocol slogan data into a byte-level sub-word data set through a word segmentation device; pre-training a mask language model through a byte-level sub-word data set, learning a contextual relationship between word meanings and sub-words, optimizing the stability of embedding to dynamic contents through a supervision and comparison loss fine tuning method, and generating slogan-level embedding suitable for fingerprint identification of downstream task equipment; performing dimension reduction clustering on the slogan-level embedding to obtain device class clusters, extracting a common sub-character string from each device class cluster, and converting the common sub-character string into a regular expression device fingerprint; and comparing the regular expression device fingerprint with the existing fingerprint database, and updating the fingerprint database. By adopting the technical scheme of the invention, automatic generation of the active detection device fingerprint can be realized through active detection and technical optimization, and the manual maintenance cost of the device fingerprint is reduced.

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 present invention relates to a multi-voting word sense disambiguation method of graph sampling aggregation (Graph SAmple and aggreGatE, GraphSAGE) embedded with semantic similarity. First, the Chinese sentences containing ambiguous words are subjected to word segmentation, part-of-speech tagging, semantic class tagging, translation annotation, and Wubi code annotation. Using the sentences containing ambiguous words, as well as the word forms, parts of speech, semantic classes, translations, and Wubi codes contained in the two lexical units on the left and right of the ambiguous word as disambiguation features and as nodes to construct five disambiguation feature graphs. Calculate the semantic similarity between the target ambiguous word and the adjacent vocabulary using the "Thesaurus of Chinese Synonyms", vectorize the features using the Word2Vec tool and the Doc2Vec tool, and embed the semantic similarity weights into the feature vectors. Optimize the multi-GraphSAGE neural network with the training corpus, and use the optimized multi-GraphSAGE neural network combined with the perceptron linear classifier to obtain the semantic classification results of each disambiguation feature graph, and adopt a voting mechanism to determine the semantic category of the ambiguous word. The present invention has a good word sense disambiguation effect and can more accurately judge the true meaning of the ambiguous word.

The invention discloses a data analysis method and system, electronic equipment and a storage medium, and the method comprises the steps: determining a target proxy address from a preset proxy pool, and obtaining to-be-analyzed data from a target cloud server cluster according to the target proxy address; performing first data preprocessing on the to-be-analyzed data to obtain first preprocessed data; wherein the first data preprocessing comprises word segmentation processing and word meaning network analysis processing; and performing prediction analysis on the first preprocessed data to obtain a data analysis result. According to the embodiment of the invention, the data acquisition efficiency and stability can be effectively improved, and the depth and breadth of data analysis are improved, so that the accuracy and reliability of data analysis are improved. The method can be widely applied to the technical field of data services.

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 invention discloses a customized locomotive intelligent translation system based on a Transformer model, which belongs to the technical field of locomotive machine translation systems, and includes an input module, a translation body, and an output module. The translation body is also connected to a locomotive customized translation vocabulary library, and the locomotive customized translation vocabulary library includes a vocabulary input module, a locomotive relevance evaluation module, and a sorting module. The invention can filter out words with poor relevance between the meaning of words and the daily meaning in locomotive data from a web page environment, construct a locomotive translation vocabulary library, and combine a self-attention network to perform customized translation of words with poor relevance between the meaning of words and the daily meaning in locomotive data, thereby improving the accuracy and professionalism of locomotive translation, and solving the problem that the poor accuracy of locomotive professional word translation in existing locomotive translation leads to insufficient understanding of locomotive-related technical information by locomotive personnel.

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.

A video moment retrieval method based on fine-grained information of video content includes the following steps: Step 1, construct a training set and a test set, and select the original video; Step 2, perform feature pre-extraction on the original video to obtain key frame features and in-frame objects; Step 3, construct a text feature extraction module, use a pre-trained word embedding model to map the query statement into the embedding space, complete feature extraction, and obtain text features; Step 4, construct a text part-of-speech tagging module to tag the nouns in the query statement; Step 5, construct a cross-modal feature fusion module to obtain cross-modal fine-grained content features; Step 6, construct a semantic matching module to generate relevance weights through semantic matching; Step 7, construct a moment retrieval guidance module to calculate the relevance content fine-grained features corresponding to the entire video. The present invention extracts fine-grained information in the video through an object detection network, constructs a cross-modal retrieval model, and improves the accuracy of video moment retrieval.

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.

The present invention relates to a word sense disambiguation method in which a convolutional block attention module (CBAM) is embedded in a Regnetic network. The present invention performs word segmentation, part-of-speech tagging, pinyin initial letter tagging, tone tagging and semantic category tagging on training corpus and test corpus of SemEval-2007:Task#5. The word form, part-of-speech, semantic category, pinyin initial letter and tone of adjacent lexical units with noun, verb, adjective, numeral, quantifier and pronoun parts of speech on the left and right of ambiguous words are selected as disambiguation features. The disambiguation features are vectorized by using a Word2Vec tool to obtain training data and test data; the CBAM is embedded in Regnetic network to obtain a word sense disambiguation model CBAMRegnety. The CBAMRegnety is trained by using the training data to obtain an optimized CBAMRegnety. On the optimized CBAMRegnety, the test data is disambiguated to obtain the weight of the ambiguous words in each semantic category. The semantic category with the largest weight is the semantic category of the ambiguous words. The present invention achieves good disambiguation of ambiguous words and more accurately determines the true meaning of the ambiguous words.

The present invention discloses a corpus processing method, device and storage medium. The method comprises: performing word segmentation on a sample corpus in a sample corpus set to obtain a sample field; performing semantic segmentation on the sample field to obtain at least one sample semantic original word; determining a sample semantic original set corresponding to the sample field according to the sample field and the sample semantic original word contained in the sample field, wherein the sample semantic original set includes at least one sample semantic original, each sample semantic original includes a semantic original identifier and a semantic original feature, and the semantic original feature is used to indicate the feature corresponding to the sample field or the sample semantic original word and the semantic original identifier; determining a target semantic original dictionary according to the sample field and the sample semantic original set corresponding to the sample field. The present invention solves the technical problem of low accuracy in corpus annotation based on a word meaning knowledge base.

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.