76 results about "Language semantics" patented technology

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

The invention discloses an artificial intelligence speech recognition system, and the system comprises a multi-modal feature extraction module which employs an improved Conformer architecture to synchronously extract the time-frequency features and text embedding vectors of speech signals; the joint training module is used for performing joint optimization on ASR and NMT loss functions through an adversarial training strategy, learning voice recognition and machine translation tasks at the same time through joint training, and completing direct mapping from voice features to a target language; the context perception translation engine is used for integrating an attention mechanism of a pre-training language model, carrying out deep coding on the extracted speech features and generating cross-language semantic representation; the self-adaptive post-processing module is used for dynamically optimizing an output result by adopting a reinforcement learning framework, dynamically adjusting the output result according to a reward function, and optimizing translation quality and a speech synthesis effect; the dynamic language recognition module is a real-time language classifier based on a Wave2Vec 2.0 framework and is used for recognizing the language of the input voice in real time; and the incremental field adaptation module is used for quickly updating a field term library by using a LoRA fine tuning technology.

The invention relates to the technical field of cross-language information processing, and provides a cross-language text fusion intelligent alignment method and system.The cross-language text fusion intelligent alignment method comprises the steps that a hierarchical alignment model is constructed through preprocessing and label recognition of multi-coding-type texts, deep semantic feature extraction and labeling of a multi-language pre-training model and text semantic and format information analysis; transform is taken as a core, cross-language semantic association is enhanced through a multi-head attention mechanism, character-level and paragraph-level format collaboration is realized through label weight allocation and condition constraint, the format alignment accuracy is obviously improved in a multi-language mixed typesetting scene, and the multi-language mixed typesetting efficiency is improved. The analysis capability of the model on the structured information can be extended to layout relation processing of texts, images and tables, so that the comprehensive alignment efficiency in a multi-modal fusion scene is remarkably improved. The accuracy of text and label fusion is guaranteed, the problem of confusion of messy codes and labels is avoided, and high-precision cross-language text alignment from semantics to formats, from single mode to multiple modes and from semantics to formats is achieved.

The invention relates to the technical field of natural language processing, and discloses a generated text quality processing method and system based on a large language model, and the method comprises the steps: constructing a logic topological graph and a Laplacian matrix of an original generated text, and extracting a feature value sequence; recognizing a logic bearing wall based on the attention gradient and generating an anti-fact contrast text; calculating a logic collapse index by combining the difference between the map characteristic values of the original text and the contrast text and the logic polarity overturning condition; and judging the quality of the generated text according to the logic collapse index, blocking the text which does not pass the judgment, and shaping and resampling the output probability value of the error position by utilizing the spectrum difference information to generate a new text. The method does not need to depend on an external knowledge base, quantifies the stability of text logic through anti-fact interference and spectrum analysis, and identifies a high-risk illusion text; and a wrong logic path is automatically corrected through Logits shaping, so that the logic self-consistency of the generated content is improved on the premise of ensuring the semantic smoothness of the text language.

The invention discloses an RGBL tracking method based on target prior autoregression query. The RGBL tracking method comprises the steps that RGBL data sets are collected and aligned, and a training set and a test set are constructed; the method comprises the following steps: on the basis of an RGB tracking network AQATrack of autoregression query, constructing an RGBL tracking model based on target prior autoregression query; by introducing language and visual semantic tokens, learning target features of each mode; designing a language semantic token enhancement module to enhance the target features of the language semantic token, and designing a target feature extraction module and a visual semantic token feature enhancement module to improve the target features of the visual semantic token; and fusing language and visual semantic token features in a decoder, outputting a multi-modal semantic token feature, and using the multi-modal semantic token feature as target prior for query with an initialization value of zero, and capturing spatio-temporal information in an autoregression learning mode. According to the invention, by introducing the target prior, the target features in the spatio-temporal information can be captured more effectively in the initial stage, so that the target positioning and tracking process of the tracker is accelerated.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention discloses a neck-hung sign language interpretation device and a sign language semantic recognition interpretation method thereof.The neck-hung sign language interpretation device comprises a wearing support which defines a neck surrounding space and comprises a middle connecting arm and two shells, and the two shells are connected to the two sides of the middle connecting arm respectively; the image acquisition unit is arranged on the front side of the shell, comprises a three-dimensional depth camera and an adjusting mechanism for driving the three-dimensional depth camera to rotate, and is used for dynamically tracking and acquiring continuous sign language videos; the processing module is integrated in the shell, comprises a main control processor and a memory, and is used for operating a sign language recognition algorithm; the voice output unit is electrically connected with the main control processor and is used for playing the translation result; and the power supply unit supplies power to the image acquisition unit, the processing module and the voice output unit. Gesture languages used by the deaf-mute for communication are converted into natural languages which can be easily understood by normal people, the deaf-mute group is helped to speak, and the problem of information output when the deaf-mute communicates with the normal people is solved.

The invention relates to the technical field of artificial intelligence, provides a multi-language task execution method, device and equipment and a medium, is applied to financial and medical health care service scenes, and can acquire text data and visual action data of multiple languages according to an execution instruction and perform preprocessing to realize standardized processing of multi-modal data; constructing a cross-language word vector semantic space based on adversarial training according to the multi-language features to realize preliminary word vector alignment; performing multi-language semantic alignment on the multi-language features according to a cross-language word vector semantic space, further breaking language barriers, and realizing depth mapping and alignment among different language semantics; a multi-language culture knowledge graph is constructed, so that culture knowledge is introduced, and the culture perception ability is improved; fusion is carried out through a gating fusion mechanism, fusion features with language attributes and cultural attributes can be obtained, and therefore multi-language tasks can be executed more accurately by integrating multi-language information and cultural knowledge.

The invention discloses a digital library index retrieval method and system, and relates to the technical field of natural language process.The method comprises the steps that a query statement input by a user is received, the semantic offset degree of the query statement in a cross-language environment is evaluated based on a multi-language semantic mapping mechanism, and a corresponding retrieval risk level is determined according to the semantic offset degree; a query statement is complemented and enhanced by introducing a multi-language semantic anchor point and concept association relationship; and performing semantic matching and scoring according to the compensated query content and the target literature, and executing a dynamic index decision operation based on a set risk level and an integrity score. According to the method, the multi-language semantic anchor point diagram is constructed, the edge weight path scoring function is combined, accurate and credible evaluation is carried out on the semantic compensation path, and the compensation result is more stable and controllable. Through semantic anchor point path extension and a multi-hop semantic propagation mechanism, original query semantic expression is complemented, and the perception ability of a multi-language retrieval system for weak explicit semantics is improved.

The invention provides a method and system for pre-training a sign language understanding framework based on semantic enhancement of skeletons, and relates to the technical field of sign language recognizing.The method comprises the steps that sign language video data and skeleton sequences and text data matched with the sign language video data are obtained; extracting a skeleton key point sequence from the sign language video data, modeling to form skeleton features, and performing word segmentation processing on a text; in a pre-training stage, skeleton features and word segmentation texts are input into a fusion network to generate bidirectional enhanced features, and global and local similarities are obtained through double-layer semantic alignment; calculating the comparison loss based on the similarity, and coordinating the weight through the balance parameter to obtain the hierarchical loss; executing the matching task and the language modeling task to obtain corresponding loss, weighting and combining the three types of loss into pre-training total loss, and adjusting parameters to complete training; and finally, part of parameters are optimized in combination with specific task types in a fine tuning stage, and enhanced understanding of sign language semantics is realized. The sign language understanding accuracy is improved.

The invention provides a knowledge editing method based on multilingual semantic retrieval, and belongs to the technical field of natural language process.The method achieves cross-lingual knowledge updating through the two stages of multilingual knowledge retrieval and context editing and comprises the steps that firstly, a multilingual retrieval model based on XLM-R is used for mapping queries and knowledge base entries to a shared semantic space, and then the shared semantic space is used for conducting context editing; semantic correlation is judged through a classifier, and target language knowledge is retrieved; and splicing the retrieval result and the query in a zero sample or small sample mode to generate a prompt template, and inputting the prompt template into a large language model to complete editing. The method is characterized in that monolingual limitation is broken through, collaborative updating of 12 languages is supported, a retrieval-editing decoupling architecture is adopted to achieve model irrelevant adaptation, and retrieval and context learning are fused to improve editing accuracy. The method can efficiently correct multi-language large model fact errors, is suitable for scenes such as search engines and intelligent customer services, avoids the cost of full-model retraining, and remarkably improves the efficiency and accuracy of cross-language knowledge updating.

The invention discloses a semantic analysis and recognition method based on artificial intelligence, particularly relates to the field of semantic analysis, and is used for solving the problems of frequent semantic offset and inconsistent translation results caused by difficulty in accurate recognition and disambiguation of cultural load words in existing cross-language text processing. The method comprises the following steps: performing text structure decomposition and culture metaphor analysis on a cross-language contrast corpus, identifying culture load words with specific culture meanings, further obtaining semantic vector sets of the culture load words in a source culture context and a target culture context, and calculating a semantic escape distance between the culture load words and the target culture context to generate an escape path; and training a context disambiguation model in combination with the escape path and the context, so that the model can output final semantic probability distribution of the culture load words in the target culture when the to-be-analyzed text is input, and generates a semantic mapping prompt in a cross-language semantic conversion process based on the probability distribution. Therefore, cross-culture accurate semantic analysis and prompt of the culture load words are realized.

The invention discloses a multi-language AI-based personalized ascending plan dynamic generation system, which comprises a multi-language semantic interaction module, a dynamic gradient prediction engine, a real-time policy adaptation system and a 1 + N double-track service module, and is characterized in that the multi-language semantic interaction module is used for processing Uyghur language and Kazakh language voice / text input; cross-language intention recognition is realized through a bidirectional LSTM neural network, and a Uighur-Chinese bilingual contrast policy lexicon covering college entrance examination, examination and research and doctor stages is constructed to realize multi-language policy semantic alignment; the dynamic gradient prediction engine is used for generating a volunteer scheme including three levels of sprint-adaptation-stability for college entrance examination, generating a sprint-adaptation-stability target scheme for the college entrance examination, and generating a core target-alternative target-bottom guarantee target scheme for the blog by fusing characteristic parameters of different learning stages on the basis of an XGBoost algorithm, and generating a college scheme including sprint-adaptation-stability for the college entrance examination, the college entrance examination, the college entrance examination and the college entrance examination and the college entrance examination and the college entrance examination and the college entrance examination and the college entrance examination. The real-time policy adapts the system.

The invention discloses a DIKWP natural language analysis enhancement method combined with a large language model, and belongs to the technical field of artificial intelligence and natural language processing. The method is realized through the following steps of: firstly, performing semantic pre-analysis on natural language input of a user by utilizing a large language model (LLM), and extracting five semantic fragments of data, information, knowledge, wisdom and intention; then the content is mapped to a DIKWP semantic map node in a structured mode through a DIKWP semantic mapping template system; then semantic consistency verification and logical reasoning are carried out through an LLM and DIKWP dual-channel cooperation mechanism; and finally, iteratively optimizing ambiguous, missing or conflicting contents by means of a language-semantic interaction closed loop until a complete and consistent DIKWP semantic map is generated. According to the method, the semantic comprehension ability of a large language model and the structural reasoning advantage of the DIKWP map are effectively fused, the accuracy, consistency and interpretability of complex natural language analysis are remarkably improved, and the method can be widely applied to intelligent question answering, knowledge modeling and decision support systems.

The invention discloses a school history culture intelligent guide system and method based on a multilingual knowledge graph, and relates to the technical field of knowledge graphs and intelligent guide, and the system comprises a multilingual school history knowledge graph construction module which constructs a multilingual ontology model and generates the multilingual knowledge graph through cross-lingual semantic alignment; the edge collaborative perception interaction module is used for collecting interaction data and realizing low-delay response based on an edge-cloud collaborative strategy; the cross-language semantic retrieval and recommendation module is used for calculating semantic similarity and generating personalized recommendation; the knowledge-enhanced multilingual intelligent question and answer module constructs an enhancement prompt and generates a question and answer result; according to the school history culture international communication system and method, the problems of language barriers and single forms in school history culture international communication are solved, and the tour guide service quality is improved.

The invention provides a dynamic sign language time sequence modeling method and system based on an attention mechanism, belongs to the technical field of sign language recognition and man-machine interaction, and is used for solving the problems of poor sign language semantic recognition scene adaptability, insufficient personalization and inaccurate semantic feature capture in the related technology. According to the method, hand space coordinates and inertial data are synchronously collected and fused to generate a double-domain time sequence tensor, a four-dimensional fusion feature is constructed in combination with rhythm features and scene prior, semantic expression is enhanced through three-level attention weighting, scene adaptation is achieved based on hierarchical meta-parameters, and the accuracy of scene matching is improved. Personalized optimization is achieved through hand feature fingerprints and full-link parameter feedback, and precise sign language semantic modeling, scene self-adaption and individual adaptation are achieved.

The invention discloses a key information extraction and analysis method suitable for foreign trade English negotiation voices, and the method comprises the steps: obtaining the historical voices of foreign trade English negotiation in multiple industries, extracting the voice features, carrying out the text transcription, and generating a first tag data set; training a round semantic clustering model to realize automatic classification of negotiation rounds and clustering of similar issues; then in combination with an intention-entity label labeled by the cross-language semantic understanding model, training an intention-entity bidirectional mapping model, and outputting associated data; training a rule-oriented conflict traceability model by combining a cross-border trade rule base, and identifying information conflict points and complementary points; constructing a key information priority evaluation model, and outputting a key information list sorted according to scores; and finally, generating a structured analysis report and dynamic negotiation strategy guidance. Through multi-model cooperation and foreign trade scene adaptation design, key information can be accurately extracted, intentions and entities can be associated, rule conflicts can be detected, negotiation information processing efficiency and risk management and control capability can be improved, local or cloud deployment can be flexibly adapted, and requirements of different foreign trade enterprises can be met.

Described is a system and method for training a multilingual semantic parser. A method includes receiving, by a multilingual semantic parser, a multilingual training dataset, wherein the multilingual training dataset includes pairs of utterances and meaning representations from at least one high-resource language and at least one low-resource language and wherein the multilingual training dataset is initially a machine-translated dataset, training, the multilingual semantic parser, by translating the utterances in the multilingual training dataset to a target language; and iteratively performing selecting, by an acquisition functions estimator, a subset of the multilingual training dataset for human translation, updating the multilingual training dataset with the human-translated subset of the multilingual training dataset with, and retraining, the multilingual semantic parser, with the updated multilingual training dataset.

The present application relates to a large model Lao translation method and system based on agent cooperation and cross-language semantic alignment, belonging to the field of natural language processing and machine translation. In the independent multilingual translation generation stage, the target Lao translation corresponding to the source sentence, the reference language translation and the linguistically similar language translation are generated respectively; cross-language semantic alignment and correction are carried out, the English translation is taken as the semantic alignment reference, the Thai translation provides morphological and syntactic guidance, and through the iterative information interaction between agents, the Lao translation is continuously evaluated and corrected; in the semantic clarification and strengthening stage, the key semantic fragments of the source sentence are extracted by analyzing the agent, the overall structure is simplified, and the Lao agent is assisted to generate the final high-fidelity translation result. The present application significantly improves the semantic accuracy and structural rationality of the Lao translation result; effectively solves the semantic illusion problem easily produced by the large language model in the Lao machine translation scene due to data scarcity.

The invention discloses a multi-language deep synthesis voice adaptive detection method in cross-border communication, and the method comprises the steps: S100, receiving a cross-border voice stream and associated metadata in real time through a communication protocol interface, the metadata comprising equipment information, a network protocol packet header and session time sequence information; s200, constructing a multi-modal feature extraction pipeline, wherein multi-modal features comprise an audio mode, a text mode and a behavior mode; s300, inputting the features into a multi-modal adaptive fusion engine: outputting a language tag and confidence c through a language recognition module, and dynamically adjusting modal weight; feature fusion is realized by adopting a gating multi-mode unit; s400, executing by a detection decision-making layer, and S500, when the detection confidence coefficient is gt; and when 90%, incremental learning is triggered. According to the method, weight dynamic allocation driven by languages is adopted, and cross-language BERT semantic verification is combined, so that the low-resource language detection accuracy is greatly improved, VPN disguise is effectively recognized, the generative attack recognition rate is improved, and the success rate of confronting sample attacks is greatly reduced.

The application discloses a machine translation quality estimation method based on multi-semantic space, which is divided into machine translation quality estimation model training and machine translation quality estimation. The model training steps are as follows: generating pseudo reference translation and back translation by using a dialogue large language model; extracting machine translation quality features in the source language semantic space; extracting machine translation quality features in the target language semantic space; extracting machine translation quality features in the cross-language semantic space; extracting a machine translation quality feature vector in the multi-semantic space; predicting a machine translation quality score based on the multi-semantic space; and training a machine translation quality estimation model based on the multi-semantic space. The machine translation quality estimation method steps are as follows: normalizing a source language sentence and machine translation to be quality estimated; inputting the normalized source language sentence and machine translation into the trained machine translation quality estimation model based on the multi-semantic space, and predicting a machine translation quality score.

The application relates to the technical field of speech recognition, and discloses a cross-modal attention fusion method and device based on voiceprint features and language semantics, which comprises the following steps: performing a feature extraction operation on speech data to obtain voiceprint features; based on a sentiment perception mask mechanism and a context sentiment memory unit, fusing sentiment features into text data to obtain semantic features; constructing a cross-modal relationship graph of the voiceprint features and the semantic features; determining a time sequence dependency relationship between the voiceprint features and the semantic features based on the edge weights generated by the cross-modal relationship graph; projecting the voiceprint features and the semantic features into the same semantic space based on the time sequence dependency relationship, and performing a feature reconstruction fusion operation based on a reconstruction loss function to obtain voiceprint and semantic fusion features; and adjusting a dialogue strategy determined based on the voiceprint and semantic fusion features; and using the adjusted dialogue strategy to generate interactive response data with historical interactive data, so that a high-accuracy and personalized speech interaction experience is realized.

The invention discloses a big language model security test method and system based on a small language cross-language attack. The method comprises the steps of constructing a small language selection strategy library, designing a cross-language semantic conversion engine, constructing a multi-dimensional language barrier, implementing a self-adaptive language switching mechanism and establishing a cross-language security evaluation system. According to the method, through dual language barriers of minority language input and Chinese output, a traditional single-language security detection system is effectively bypassed, attack content is presented in a minority language form, but a target model is forced to reply with Chinese, and a language understanding gap is established between input and output; the dynamic language switching strategy realizes intelligent cross-language attack, and the attack method can be adjusted in real time according to the defense response of the target model. According to the method, the adaptability, the concealment and the overall test effect of attacks are remarkably improved, and a systematized and efficient technical means is provided for large model security assessment in a multi-language environment.

The embodiment of the invention provides an ancient Chinese language translation method and device, electronic equipment and a readable storage medium, belongs to the technical field of artificial intelligence, and is applied to financial scenes and medical scenes. The method comprises the following steps: performing ancient language knowledge reasoning on ancient language semantic features from an ancient language knowledge graph to obtain ancient language interpretation knowledge; performing ancient language context analysis on the ancient language semantic features to obtain ancient language context information; performing translation term detection on the ancient language semantic features according to the ancient language context information to obtain ancient language translation terms; and determining an ancient language translation strategy based on the ancient language context information and the ancient language semantic features, and translating the target ancient language based on the ancient language interpretation knowledge, the ancient language context information, the ancient language translation terms and the ancient language translation strategy. According to the embodiment of the invention, the target ancient Chinese is translated based on the ancient Chinese interpretation knowledge, the ancient Chinese context information, the ancient Chinese translation terms and the ancient Chinese translation strategy, the professional knowledge of the ancient Chinese and the cultural background of the ancient Chinese are comprehensively considered, and the ancient Chinese translation accuracy is improved.

The application discloses an RGBL tracking method based on target prior autoregressive query, which comprises collecting and aligning an RGBL dataset, constructing a training set and a test set; taking an autoregressive query RGB tracking network AQATrack as the basis, constructing an RGBL tracking model based on target prior autoregressive query; introducing language and visual semantic tokens to learn target features of each mode; designing a language semantic token enhancement module to strengthen the target features of the language semantic tokens, and designing a target feature extraction module and a visual semantic token feature enhancement module to improve the target features of the visual semantic tokens; fusing the language and visual semantic token features in the decoder, outputting multi-modal semantic token features, and taking the multi-modal semantic token features as target prior to initialize the query with a value of zero, and capturing spatiotemporal information in an autoregressive learning mode. The application can more effectively capture target features in spatiotemporal information in the initial stage by introducing target prior, thereby speeding up the target positioning and tracking process of the tracker.

The application discloses a building short-term load prediction method based on a lightweight language model, and relates to the field of building short-term load prediction.The application comprises the following steps: performing data preprocessing on historical multivariate time series data, dividing the processed historical multivariate time series data into multiple non-overlapping time segments, processing each time segment, outputting text embedding and numerical embedding, splicing and linearly aligning semantic embedding vectors and numerical embedding vectors, generating multivariate fusion embedding through a multivariate block feature fusion mechanism, and inputting the multivariate fusion embedding sequence into a frozen pre-trained large language model.The application effectively bridges the representation gap between the numerical time series mode and the language semantic space by converting timestamp information and statistical features into structured natural language descriptions and introducing a pre-trained language model for coding, so that the model can better understand the periodicity, trend and context dependence in the load data.

The application relates to the technical field of artificial intelligence, affective computing and computer-assisted teaching, in particular to a sign language emotion recognition and teaching feedback method based on machine learning, which collects learner sign language videos and extracts face, hand and posture holographic key points; action semantic features and emotion expression features are obtained in parallel by using a double-branch time sequence coding network; the sign language content and the emotion state containing continuous values of valence-arousal-dominance and discrete categories are obtained by a semantic recognition subnetwork and an emotion recognition subnetwork respectively; the recognition result is compared with standard semantics and emotion labels, and emotion intensity, naturalness, semantic matching degree scores and comprehensive quality scores are calculated; emotion correction instructions, reinforcement learning training sequence planning and key point trajectory visualization feedback are generated based on the scores; and teaching strategies are iteratively improved through closed-loop optimization and emotion resonance models. The application realizes synchronous recognition and quantitative evaluation of sign language semantics and emotions, and provides an adaptive feedback means for sign language emotion expression ability training.

The invention provides an image-oriented Chinese ancient poetry recommendation method and system based on multi-modal learning. The method comprises the following steps: constructing a structured ancient poetry corpus; generating multi-dimensional semantic tags for the ancient poetry in the corpus, and encoding each ancient poetry semantic tag into an ancient poetry tag semantic vector by using an ancient poetry masking language model trained based on an ancient poetry corpus; receiving an image input by a user; according to the ancient poetry semantic tags, endowing image semantic tags conforming to the image of the image with the image semantic tags by using a cross-modal alignment model, and encoding each image semantic tag into an image tag ancient language semantic vector by using the ancient language masking language model; and calculating the similarity between the image tag ancient language semantic vector and each ancient poetry tag semantic vector to obtain alternative ancient poetry tags, and performing reordering to obtain the ancient poetry tag which most conforms to the image artistic conception and the corresponding ancient poetry to be output as a recommendation result. According to the method, ancient poetry recommendation can be carried out for images.

The invention relates to the technical field of contract risk analysis based on cross-language semanteme, and discloses a contract risk analysis method and device based on cross-language semanteme, equipment and a medium. The method comprises the steps that contract terms of different languages are converted into standardized legal semantic vectors through a multi-language semantic model, according to the method, analysis deviation caused by language and culture differences is overcome, the understanding accuracy of the small language contract is improved, the knowledge graph is constructed by utilizing the law domain legal provision vectors associated with the target law domain, the timeliness of the legal basis of a matching link is ensured, and the conflict risk between contract terms and laws and regulations can be found. The method has the beneficial effects that the comprehensiveness and objectivity of risk identification are improved, the qualitative grading and quantitative evaluation of the risk are also realized, a direct risk evaluation result is provided for a decision maker, the efficiency of legal affair examination and the risk control capability are improved, and the automation, precision and real-time performance of contract risk analysis are realized.