107 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 an event extraction system and method based on a pre-training model and word meaning enhancement. A field word vector acquisition module obtains a field word vector; the data set construction module obtains a specific field text sequence data set; the event extraction model construction module constructs an event extraction model; the training module takes the field word vector as initial setting of training in a Soft-lexcon sub-model in an event extraction model, and trains the event extraction model by utilizing a labeled specific field text sequence data set; and the event extraction module predicts a tag sequence result of the to-be-detected specific field text sequence data set by using the trained event extraction model. According to the method, the pre-trained LERT sub-model and the Soft-lexcon sub-model are utilized to bring the field word information of the field word vector into character representation, and the CRF layer is introduced into the model, so that the accuracy of label labeling in the event extraction task is improved, and the problems of scarcity of labeling data, high specialty and complex context in specific field event extraction are solved.

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 relates to a software test case sharing and multiplexing method and system based on semantic understanding search, and belongs to the technical field of software testing. According to the method, defined entities, attributes of the entities and relationships among the entities are extracted from massive software test cases, a software test case knowledge graph is constructed, and the extracted knowledge is fused under the same framework specification to form a high-quality knowledge base. Meanwhile, word segmentation understanding based on semantic understanding is carried out on search words of the user, intention recognition, semantic ambiguity elimination and word meaning expansion are carried out on the search words after word segmentation through the constructed knowledge graph, search results are clustered and sorted according to service weights and provided for the user, and sharing and reuse from semantic search to software test cases are achieved. The testing efficiency and quality are improved, and repeated work and testing cost are reduced. Meanwhile, the method has high practicability and universality and can be applied to different software testing scenes and projects.

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 introduces a multi-stage similar function point description text retrieval and comparison algorithm which combines the excellent semantic understanding ability of a large language model with the traditional vocabulary comparison technology. By adopting a multi-language synonym transcription technology, a possible synonym expression text library is constructed in advance; and then, the texts are converted into vector forms by utilizing a vector library technology, and indexes are established, so that the retrieval speed and accuracy are improved. And finally, by fusing a large language model and a traditional word meaning comparison algorithm, the similarity between texts can be comprehensively evaluated. According to the method, the core significance of the function point description text can be accurately extracted, and similar function points and different function points can be effectively distinguished, so that more accurate and efficient function point comparison is realized. The application of the system helps to reduce the work of repeated construction in the informatization construction process.

The invention discloses a corpus extension method, corpus extension equipment and a storage medium. The corpus extension method comprises the following steps: performing word segmentation on an original corpus to obtain original vocabularies; determining score influence factors matched with the candidate replacement vocabularies based on the word meaning quantity corresponding to the candidate replacement vocabularies and / or the original vocabularies; calculating a replacement recommendation score corresponding to the candidate replacement vocabulary by using the score influence factor, and selecting the candidate replacement vocabulary of which the replacement recommendation score meets a preset condition as a target replacement vocabulary corresponding to the original vocabulary; and replacing the original vocabularies in the original corpus with the target replacement vocabularies corresponding to the original vocabularies to obtain the extended corpus, so that score influence factors matched with different candidate replacement vocabularies can be accurately obtained, the calculation accuracy of replacement recommendation scores is improved, the selection accuracy of the target replacement vocabularies of the original vocabularies is further improved, and the user experience is improved. On the premise of increasing the corpus quantity, the situation that the extended corpus has semantic change relative to the original corpus is reduced, and the quality of the extended corpus is ensured.

The invention discloses a data query method and device, a storage medium and a computer program product. The method comprises the steps that a target text input by a target object is obtained, and the target text comprises a plurality of words; determining the meaning and the part of speech of each word in the target text, and determining the text type of the target text according to the meaning and the part of speech of each word, the text type including a query text or a non-query text; under the condition that the text type is a query text, generating a target query statement for an analysis result of the target text based on a pre-trained large language model; and outputting a query result obtained by querying in a preset database by using the target query statement. According to the method and the device, the technical problems of relatively low query efficiency and relatively high labor cost caused by manually writing query statements to query data in related technologies are solved.

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 is suitable for the technical field of Chinese semantic parsing, and provides a Chinese semantic parsing method and parser fusing part-of-speech characteristics, and the method comprises the following steps: analyzing a document structure, and carrying out sentence segmentation on a document body through an NLP technology; performing word segmentation on the sentence segmentation result by adopting a word segmentation tool to obtain a word segmentation result, part of speech and coordinate information of the word segmentation result; inputting the word segmentation result and the part-of-speech into an encoder module for encoding to obtain a semantic feature vector; and sequentially inputting the semantic feature vectors obtained by encoding into a decoder module according to a word segmentation sequence to obtain a local semantic analysis result of the sentence, and converting the local semantic analysis result into semantic feature vectors to serve as input of next decoding until a complete semantic analysis result of the sentence is obtained. According to the method, the word meaning feature vector and the part-of-speech feature vector are fused, and feature association between nodes is enhanced, so that the accuracy of sentence semantic analysis is effectively improved.

The invention discloses a digital human interaction system based on artificial intelligence optimization, which comprises a signal acquisition module, an emotion processing module, an image voice processing module and a feedback correction module, and is characterized in that the signal acquisition module acquires startup signal data, and voice data and text data after startup; the emotion processing module carries out word meaning interpretation on the voice data and the text data to obtain result data, and obtains the result data, corresponding emotion data, response text data and corresponding response emotion data; the image voice processing module generates an appearance image model of the digital person and a sound image model of the digital person, fuses the appearance image model and the sound image model, and generates a target digital person; and the feedback correction module is used for evaluating the feedback of the user, correcting the emotion processing module according to an evaluation result, correspondingly adjusting the emotion processing module according to the emotion change of the user, and providing a more humanized digital human dialogue service.

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 present application discloses a cross-language language model pre-training method and device, a cross-language named entity recognition method based on transfer learning, a cross-language sentiment analysis method based on transfer learning, a cross-language sentence relationship inference method based on transfer learning, a computer storage medium and an electronic device, wherein the pre-training method comprises: inputting text information into a pre-trained language model, obtaining a first context word vector of a word in the text information; selecting a word sense vector matching the first context word vector in a constructed word sense clustering set; determining a second context word vector corresponding to the word sense vector in a context word vector set output by the pre-trained language model; determining the word sense vector probability of the word based on the second context word vector and the first context word vector. Thus, the context word vectors are aligned at the word sense level granularity.

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.

To reduce user dissatisfaction and to reduce the burden on a superintendent, etc.SOLUTION: An elevator operation control adjustment device according to an embodiment includes: a voice collection unit that collects voices uttered by elevator users at a landing or in a car of an elevator; and an operation control adjustment unit that, based on a word meaning dissatisfaction included in the voices collected by the voice collection unit, adjusts control to be applied to the operation of the elevator.SELECTED DRAWING: Figure 1

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 disclosure provides a Chinese spelling correction method, including: obtaining a text sequence, a pinyin sequence and a picture sequence of a Chinese input text file; based on the text sequence, the pinyin sequence and the picture sequence respectively extracting word meaning features, phonetic features and glyph features of the Chinese input text file; integrating the word meaning features, phonetic features and glyph features; based on the integrated word meaning features, phonetic features and glyph features, performing a correctness prediction, a pinyin prediction and a character prediction on the Chinese input text file to obtain the corrected Chinese output text file; performing rationality judgment on the Chinese output text file to obtain the final Chinese text file. The method is based on multi-modal neural networks and language models to realize the recognition and correction of word meanings, phonetics and glyphs in Chinese spelling, thus effectively improving accuracy and practicality.

The present invention belongs to the technical field of multimodal joint modeling for dialog intention processing. A method and system for coreference resolution and word sense disambiguation based on visual-text joint modeling is provided. Based on text enhancement features and video enhancement features, a cross-attention feature network is used to adjust features to obtain adjusted features. The adjusted features are coordinated using a distribution similarity metric, and coreference resolution prediction is performed on the coordinated features to obtain coreference resolution results corresponding to the input text. The present invention fuses text features and visual features through a cross-modal attention mechanism to generate an accurate joint representation, capture the deep semantic relationship between text and video content, and thus construct an efficient word sense disambiguation model, overcoming the problem of poor disambiguation performance of existing methods when facing complex and changeable natural language query statements from users.

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.