58 results about "Character vector" patented technology

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The present disclosure relates to a mathematical formula recognition method and apparatus, and an electronic device and a readable storage medium. The method comprises: acquiring an original image containing a mathematical formula; inputting the original image into a formula recognition model, so as to obtain a predicted character vector that is output by the formula recognition model; the formula recognition model performing enhancement processing on the basis of the correlation between pixels in a region where the formula in the original image is located, so as to obtain an image encoding vector, and performing recognition processing on the image encoding vector to obtain the predicted character vector; and generating the mathematical formula in the original image on the basis of the predicted character vector. In the present embodiment, by means of a formula recognition model, enhancement processing is performed on pixels in a region where a formula in an original image is located, such that the correlation between characters in the original image can be acquired, thereby facilitating an improvement in the accuracy of predicting a character vector, and further improving the accuracy of reducing a mathematical formula.

The invention relates to the technical field of natural language processing, in particular to a multi-granularity Chinese medical named entity recognition method and system for dynamic knowledge enhancement, and the method comprises the steps: constructing a medical term knowledge base containing a plurality of Chinese medical terms, and generating a term representation vector for each term; the method comprises the following steps: for an input Chinese medical text sequence, extracting a character initial embedding vector, fusing a word-level embedding vector of a word where the character initial embedding vector is located and an adjacent word-level embedding vector to obtain a multi-granularity feature fusion vector, and encoding to obtain a character vector containing context semantics; performing semantic alignment optimization based on the character vector and the term vector to obtain a term information enhanced character vector, and dynamically updating the medical term knowledge base according to the term information enhanced character vector; and taking the updated term vector as knowledge priori, performing multiple rounds of screening on the text candidate segments, and outputting a medical named entity recognition result. The method can effectively improve the recognition accuracy of nested entity, isomorphism and ambiguity terms, reduces boundary division errors and category confusion, and improves the reasoning efficiency.

The invention discloses an implementation method and system for AI and Agent agents to perform autonomous exploration and produce content, and relates to the technical field of AI and Agent agents, and the implementation method comprises the following steps: receiving an Agent identity file, an exploration target keyword, a narrative style parameter and an observation preference parameter configured by a user at a client, and generating Agent metadata containing character vectors and behavior tendencies; generating a virtual city model with a spatial topological structure based on GIS data, a POI map and historical and cultural corpora, and endowing buildings, roads and NPCs in a city with queriable semantic tags and interactive states to form a city-level semantic environment; by constructing a city-level semantic environment and a multi-agent collaborative exploration mechanism, autonomous perception, hierarchical decision and dynamic narrative generation of the AI Agent in a highly structured virtual city are realized, and by means of deep fusion of GIS data, POI atlas and historical and cultural corpora, a real geographic basis and cultural connotation are given to a virtual space, and immersion and logic coherence are improved.

The invention belongs to the technical field of public security management, and relates to an unmanned aerial vehicle public security inspection method, system and device. In public security inspection, a traditional method is limited by the environment and is difficult to accurately extract figure targets and process dynamic distance relations among figures. A target inspection area image is collected through an unmanned aerial vehicle, a grid image is constructed by using a feature extraction method, and a character target is extracted by combining a character target feature image set; establishing a character vector field by means of a capsule neural network, analyzing a distance relationship, tracking the change of the distance relationship, and analyzing and processing through a long-short-term memory network to obtain a constant distance interval; and finally, judging whether the person is normal according to the distance relationship among the persons and prompting. According to the invention, people can be accurately identified, the distance relation is dynamically analyzed, abnormity is judged, and public security inspection efficiency and prevention and control capability are effectively improved.

Metadata for highlights of a video stream is extracted from card images embedded in the video stream. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. Card images embedded in video frames of the video stream are identified and processed to extract text. The text characters may be recognized by applying a machine-learned model trained with a set of characters extracted from card images embedded in sports television programming contents. The training set of character vectors may be pre-processed to maximize metric distance between the training set members. The text may be interpreted to obtain the metadata. The metadata may be stored in association with the portion of the video stream. The metadata may provide information regarding the highlights, and may be presented concurrently with playback of the highlights.

The application provides an entity extraction method, device and equipment and a computer readable storage medium. The method comprises: obtaining at least one character vector and at least one extended word vector contained in a text to be extracted; the at least one extended word vector contains at least one preset entity vector; the at least one preset entity vector is the vector information of the entity corresponding to the text to be extracted in a preset entity dictionary; performing encoding and decoding transformation based on the at least one character vector and the at least one extended word vector to obtain at least one target entity corresponding to the text to be extracted; and the at least one target entity is used to realize natural language processing of the text to be extracted. Through the application, the efficiency of entity extraction can be improved on the basis of ensuring the accuracy of entity extraction.

The embodiment of the present disclosure discloses a text entity relationship analysis method, device, electronic equipment and readable storage medium. The text entity relationship analysis method comprises: a vector obtaining step, obtaining a vector combination comprising an entity vector corresponding to a character in a text, wherein the entity represented by the entity vector comprises at least one character; a vector splicing step, splicing the vectors in the vector combination for the character in the text to obtain a character splicing vector; a pooling step, performing pooling calculation on the character splicing vector to obtain an entity pooled vector; and an entity relationship determining step, splicing any two entity pooled vectors as an entity pair vector, and classifying the entity pair vector to determine the entity relationship.

The present invention relates to a Chinese named entity recognition method based on local and global character representation enhancement. Existing Chinese named entity recognition methods model it as a character-based sequence labeling problem, but a single Chinese character vector is difficult to represent independent semantics, which brings about entity boundary and type recognition errors. The glyph structure of Chinese characters and their related domain terms contain information specific to domain entities, and effective use of this information is conducive to solving the above problems. To this end, the present invention uses an autoencoding mechanism to fuse the radical structure embedding, radical sequence embedding and contextual semantic embedding of characters to obtain a local character representation; and uses an interactive gating mechanism to combine the global domain term representation corresponding to the character with the local character representation to obtain an enhanced character representation; finally, the enhanced character representation is sent to the Bi-LSTM and CRF layers to obtain a character sequence label. Experiments on a domain Chinese named entity recognition dataset show that the present invention is effective.

The invention discloses a personalized emotion compensation method for lower limb walking intention perception of a stroke patient and a related device, and relates to the technical field of medical rehabilitation, the method comprises the following steps: synchronously collecting an electroencephalogram before a lower limb walking rehabilitation task of the stroke patient and a lower limb surface electromyogram signal in the task; preprocessing the electroencephalogram physiological signals and calculating dynamic brain function connection characteristics; preprocessing the electromyographic signals; inputting the brain function connection features into a TCN-LSTM model to obtain an initial emotion vector, and determining a comprehensive emotion vector in combination with an individual character vector converted by a psychological scale; time and amplitude scaling factors of the electromyographic signals are determined according to priori knowledge and a predefined optimization algorithm; inputting the preprocessed electromyographic signals into a convolutional network model to extract deep spatio-temporal features, performing scaling factor compensation to obtain compensated electromyographic features, inputting the compensated electromyographic features into a classifier to identify lower limb walking intention category output, and controlling rehabilitation equipment or feedback; the effect and efficiency of lower limb walking rehabilitation training of the stroke patient are effectively improved.

The present application relates to a kind of based on demonstrative word implementation financial field contract element extraction analysis method, including to document is divided into sentence, and text analysis is carried out;Initial vector representation of text character is obtained, aggregation demonstrative word is in the representation information in LSTM network layer, the similarity of demonstrative word and context other character vector is calculated, and weighted summation is carried out;Weighted information is converted into the probability distribution of each entity mark, and the optimal path is obtained by dynamic programming algorithm analysis, obtains the element entity information of text.The present application also relates to a kind of for realizing based on demonstrative word financial field contract element extraction analysis device, processor and its computer readable storage medium.The demonstrative word implementation financial field contract element extraction analysis method, device, processor and its computer readable storage medium based on the present application, compared with traditional method, guarantee element extraction accuracy, the whole time is far less than traditional method, and the efficiency of element extraction in contract can be greatly improved.

The invention provides a named entity recognition method for a high-speed railway technical improvement and overhaul project text, which comprises the following steps of: preprocessing unstructured data and semi-structured data in a picture and / or a text, and converting character-level positions of entity labels by adopting BIOES (Basic Input / Output Element Specification) labeling to obtain a technical improvement and overhaul data set; performing data enhancement on the technical improvement and overhaul data set by adopting a data enhancement strategy to obtain a data enhancement result; and inputting the text data subjected to data enhancement into an RBC fusion model, converting the text data into character vectors, carrying out a sequence labeling task to obtain a prediction label result of each character, and finally realizing an entity of a technical renovation and overhaul text through a large model prompt project method. Through preprocessing, data enhancement and an RBC fusion model, non-structured and semi-structured data in the high-speed rail technical improvement and overhaul project are efficiently identified, the problem that key information cannot be dynamically extracted through a traditional method is solved, and the method has the advantage that the information extraction efficiency and accuracy of the high-speed rail technical improvement and overhaul project are improved.

To extract metadata for highlights of a video stream from a card image embedded in the video stream.SOLUTION: Highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. Card images embedded in video frames of the video stream are identified and processed to extract text. The text characters may be recognized by applying a machine-learned model trained with a set of characters extracted from card images embedded in sports television programming contents. A training set of character vectors may be pre-processed to maximize metric distance between the training set members. The text may be interpreted to be able to obtain the metadata. The metadata may be stored in association with a portion of the video stream. The metadata may provide information regarding the highlights, and may be presented concurrently with playback of the highlights.SELECTED DRAWING: Figure 4

The invention discloses a scene character recognition method based on single vector query decoding and related equipment. The method comprises the following steps: performing visual feature extraction on an image to obtain visual features; coding the character to be coded to obtain a character vector; performing global information fusion on the visual features and the character vectors to obtain context vectors; inputting the context vector and the visual feature into a single vector decoder for decoding to obtain a target decoded character; and if the characters in the image are not completely decoded, taking the target decoded character as a to-be-coded character, and returning to the step of coding the to-be-coded character by adopting the position sensing hash coding method to obtain the character vector until the characters in the image are completely decoded, thereby obtaining a scene character recognition result. According to the method, the recognition accuracy of the model on shielded and fuzzy samples can be improved, the problems of attention drift and calculation amount increase faced by a traditional autoregression decoding method can be relieved, and the method can be widely applied to the technical field of computer vision.

The application provides a term extraction method and device, electronic equipment and storage medium, the method comprises: performing word segmentation processing on input corpus of a to-be-extracted term to obtain a plurality of candidate words; for any candidate word, based on a word embedding language model, a word vector corresponding to the target candidate word is obtained; based on a character embedding language model, a character vector corresponding to each character in the target candidate word is obtained, and a sum vector of each character vector is further obtained; according to the similarity comparison result between the word vector and the sum vector, it is determined whether the target candidate word is extracted as a term; wherein the word embedding language model and the character embedding language model are both obtained by training a pre-trained language model based on sample corpus in the vertical industry to which the input corpus belongs. Thus, manual annotation of training data is not required, the term extraction cost is reduced, and accurate extraction of terms in vertical industry corpus can be realized.

The application provides a knowledge extraction method and system based on prompt learning, embedding obtained unstructured text data to obtain a cache vector value; embedding a homogeneous prompt string to obtain a homogeneous prompt symbol vector value, and embedding a heterogeneous prompt character string to obtain a heterogeneous prompt character vector value; splicing the cache vector value, the homogeneous prompt symbol vector value and the heterogeneous prompt character vector value to obtain a spliced vector, taking the spliced vector as a cache vector value of a pre-training language model; adopting a regular matching method to obtain structured data from text data generated by the pre-training language model; the application uses automatically encoded prompt characters, automatically learns a potential semantic representation of a label, and solves a knowledge extraction problem in a general way through a generative large-scale pre-training language model, thereby improving the precision and efficiency of knowledge extraction.

The disclosure generally relates to a computer-implemented method for training a machine learning model for text generation, the method comprising inputting text into the machine learning model; preprocessing the input text to obtain a plurality of character vector representations; encoding, using an encoder, each of the plurality of character vector representations to obtain a plurality of word vector representations; generating, using a backbone model, a plurality of predictive word vector representations based on the plurality of word vector representations; decoding, using a decoder, the plurality of predictive word vector representations to obtain a plurality of character-probabilities; and updating the machine learning model based the plurality of character-probabilities. The disclosure also relates to a computer implemented method for generating text, a corresponding device, system and computer program.

The application provides a network request risk detection method, system and server, and relates to the field of network request risk detection. The method performs feature fusion on an injection keyword matching result, a network request parameter statistical result and a network request character vectorization result corresponding to to-be-detected data, dynamically optimizes continuous features in the fused feature data, and realizes accurate detection of SQL injection risks in a network request process through a lightweight classifier.

This invention discloses a work order classification method, system, device, and medium based on a multi-classification model, relating to the field of work order classification technology. The method includes: acquiring original work order text and forming a labeled sample set through expert labeling; statistically analyzing the label distribution and merging or data augmenting categories below a threshold to obtain a balanced labeled dataset; cleaning to remove non-semantic information to obtain a clean text dataset; training a static character vector model and a pre-trained dynamic character vector model to obtain static and dynamic vector weights; extracting and concatenating static and dynamic features to generate a fused feature matrix; hierarchically sampling the fused feature matrix into training and testing sets, inputting it into a Bi-LSTM-Attention-CNN multi-classification network for training to obtain a work order classification model for classifying new work orders. This invention can replace manual work order automatic labeling for tens of thousands of work orders, saving 2000 hours annually and directly providing data support for product iteration.

A method for extracting Chinese financial evaluation units based on inter-character relationships includes: encoding Chinese financial text sentences at the encoding layer to obtain character vectors; feeding the character vectors and the inter-character relationship matrix into a graph convolutional neural network model, enhancing the Chinese character relationships between evaluation elements on the character vectors, and encoding the dependency relationships; constructing a grid of character pair relationships in the evaluation text, and differentiating the words on the rows and columns of the grid using two unit convolution kernels; performing convolution operations using image convolution kernels of different sizes in the row and column directions; for elements in the grid, fusing their corresponding word codes on the rows and columns and the dependency relationship codes between the two words, determining the element's label by decoding, and completing the extraction of the evaluation unit. The present invention can solve the problems of insufficient utilization of Chinese character relationships between evaluation elements, failure to distinguish different evaluation element types when representing grid elements, and insufficient utilization of Chinese character relationships within evaluation elements.

The application provides a semantic recognition method and device, the semantic recognition method comprises: obtaining N first texts and a second text; each first text comprises a first keyword in a preset word table, and N is a positive integer greater than 1; performing first text processing on each first text through a preset text processing model to obtain M character vectors corresponding to the first keyword in each first text, and M is a positive integer greater than 1; determining a first word vector corresponding to each first text according to the M character vectors; and performing semantic recognition on the second text according to the first word vector corresponding to each first text.

The application belongs to the technical field of computer application, and particularly relates to a college major attribute entity extraction method based on a difference attention mechanism. The method takes a character vector, an external vocabulary vector and an entity text vector as keys, values and queries of the difference attention mechanism, effectively integrates the difference of external knowledge into the original text, improves the personalized representation of the character vector in different contexts, realizes better fusion, simultaneously, utilizes a CRF model to perform structure output, and further realizes the college major attribute entity extraction, attributes are assigned to the college majors, so that the candidates can conveniently select.

The invention discloses an ancient book entity recognition method and system, a medium and equipment, and relates to the technical field of ancient book named entity recognition, and the method comprises the steps: obtaining a to-be-recognized ancient book text statement; the method comprises the steps of converting an input ancient book text statement into an initial word vector, determining corresponding character features and syntactic features according to the initial word vector, and obtaining a text with context information according to the character features and the syntactic features; wherein the initial word vector is the superposition of a character vector, a fragment vector and a position vector; feature extraction and splicing are carried out on the text from the forward direction and the reverse direction, splicing features are determined, the entities and punctuations are recognized according to the splicing features, and corresponding ancient custom entity recognition results are obtained; wherein the identification result comprises an identification tag sequence and a punctuation tag sequence.

This disclosure presents embodiments of an information extraction method, apparatus, device, and computer-readable medium. One specific implementation of the method includes: acquiring target text; encoding each character in the target text to generate character vectors, obtaining a character vector sequence; determining a target probability value group corresponding to each character vector in the character vector sequence, obtaining a target probability value group sequence; generating an object vector sequence set based on the target probability value group sequence and a tag set; generating a tag sequence set based on the object vector sequence set and an object transition matrix set; and extracting object information from the target text corresponding to each tag sequence in the tag sequence set, obtaining an object information set. This implementation enables information extraction from text with overlapping information, providing convenience for applications such as text analysis.

Methods, apparatuses and computer programs for training a machine learning model to generate text and for generating text using the trained machine learning model are disclosed. The present disclosure generally relates to a computer-implemented method for training a machine learning model for text generation, the method comprising: inputting text into the machine learning model; preprocessing the input text to obtain a plurality of character vector representations; encoding each of the plurality of character vector representations using an encoder to obtain a plurality of word vector representations; generating a plurality of predicted word vector representations based on the plurality of word vector representations using a trunk model; decoding the plurality of predicted word vector representations using a decoder to obtain a plurality of character probabilities; and updating the machine learning model based on the plurality of character probabilities. The disclosure also relates to a computer-implemented method for generating text, a corresponding device, a system and a computer program.

This application discloses an image processing method, apparatus, device, and computer-readable storage medium, belonging to the field of computer technology. The method includes: obtaining character vectors corresponding to the characters in the text content to be processed and key information vectors corresponding to the key information in the text content; determining initial image features corresponding to the text content; adjusting the initial image features based on the character vectors and key information vectors to obtain target image features corresponding to the text content; and obtaining the target image corresponding to the text content based on the target image features. This method considers relatively comprehensive information, and the obtained target image can fully cover the text in the text content, resulting in a high degree of matching between the target image and the text content.

An information processing method, device, equipment, storage medium and program product are disclosed, the method comprising: obtaining a to-be-detected target sentence of a user, the to-be-detected target sentence being composed of multiple characters; determining corrected characters according to the multiple characters through an error correction model; the error correction model being obtained by training a pre-trained language model, a semantic aggregation layer network and a classifier, the pre-trained language model being used to generate character vectors, the semantic aggregation layer network being used to generate error correction vectors according to the character vectors, and the classifier being used to generate the corrected characters according to the error correction vectors; wherein the corrected characters compose a corrected sentence, which is used to support voice interaction with the user. The present application can solve the problem of complex processing and low accuracy of error detection in the prior art voice error correction method.

The present disclosure provides a traditional Chinese medical record named entity recognition method based on a multi-head attention mechanism, comprising the following steps: obtaining text data of a traditional Chinese medical record; combining character vectors and word vectors in the obtained text data and sending them into a Bi-GRU neural network for feature extraction to obtain global features; using a multi-head attention mechanism to provide potential semantic information for the character vectors and extract local features; inputting the global features and the local features into a conditional random field layer to obtain a named entity sequence labeling result of the text data; the present disclosure does not require a word segmentation operation, combines the features of characters and words to form a joint feature, controls the weights of the characters and the words using a hyperparameter, inputs the joint feature into an embedding layer, and adds spatial attention in the Bi-GRU layer to make up for the deficiency in extracting effective features, greatly improving the accuracy of entity recognition.

The present invention is used in the field of artificial intelligence and relates to the field of blockchain. It discloses a question-answer matching method, device, equipment and storage medium based on an attention mechanism. Among them, the method part includes: obtaining a user question input by a user and obtaining answer vectors corresponding to a plurality of candidate answers; inputting a character vector sequence of the user question into a BERT model to obtain a plurality of hidden state question vectors output by the BERT model; converting the plurality of hidden state question vectors based on the attention mechanism to obtain m question feature vectors for characterizing the user question; converting the m question feature vectors according to the answer vectors to obtain a user question vector corresponding to the answer vector; determining the correct answer to the user question among the plurality of candidate answers according to the matching value between the corresponding user question vector and the answer vector; the present invention improves the matching effect between the candidate answer and the user question, reduces the data processing amount in the matching process, and improves the efficiency of question-answer matching.

The invention discloses a traffic accident address analysis method and device, equipment and a medium, and relates to the technical field of artificial intelligence. An address text is analyzed into a plurality of address elements, and an address element space relation directed graph is constructed based on the address elements and corresponding space semantic confidence degrees; the spatial semantic relation in the address elements is quantized, so that the road boundary rule is captured; the character vectors and the corresponding spatial semantic feature vectors are spliced according to the distributed weights to obtain a plurality of preliminary feature vectors so as to achieve conversion and unification of boundary rules and vector spaces, then the characters and the spatial vocabularies are matched based on the correlation degree, the converted and unified characters and the spatial vocabularies are organically matched, and therefore the matching efficiency of the characters and the spatial vocabularies is improved. Therefore, organic unification of traditional discrete rule boundaries and continuous address space features is realized, the analysis efficiency of Chinese addresses in traffic accidents is improved, and a basis is provided for traffic accident processing.