26 results about "Character vector" patented technology

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 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 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 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.

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.

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 application discloses a character image infringement early warning method and device, equipment and medium, and relates to the technical field of computer vision, which comprises the following steps: frame extraction is performed on a to-be-detected video, character detection is performed on the extracted target frame, and detection information is generated; a to-be-detected vector and a to-be-detected character visual constituent element are generated based on the detection information, the to-be-detected vector is matched with a registered character, and a candidate registered character is determined; a target score is determined based on the registered character vector and the to-be-detected vector, and an element combination constraint score of the to-be-detected character image and the candidate registered character is determined; a comprehensive credibility score is determined based on the target score and the element combination constraint score, the comprehensive credibility score is aggregated, and an aggregated score is obtained; a candidate evidence package is constructed according to the aggregated score, the target score, the element combination constraint score and a registered reference graph; the candidate evidence package is reviewed, and an infringement early warning result is determined according to the aggregated score and a structured review result. The character image after modification can be accurately identified.

The embodiment of the application discloses an information extraction model training method, an information extraction method, an information extraction device, an information extraction equipment and a medium. The information extraction model training method comprises the following steps: obtaining information extraction training data; inputting the information extraction training data into a label vector output submodel of an information extraction model for label identification to obtain a label representation vector of the information extraction training data; inputting the information extraction training data into a character vector output submodel of the information extraction model for character identification to obtain a character representation vector of the information extraction training data; calculating similarity data of the label representation vector and the character representation vector; and inputting the similarity data into a classifier model of the information extraction model for classification identification to obtain an information classification result of the information extraction training data. The technical scheme of the embodiment of the application can improve the training efficiency and model precision of the information extraction model, and further improve the efficiency and accuracy of information extraction.

The application provides a Chinese event detection method based on a part-of-speech attention mechanism, which is based on a public data set. First, the sentence is divided into words using a word segmentation tool on the data set, and then the part-of-speech sequence of the sentence is obtained using a part-of-speech tagging tool. The part-of-speech sequence of the sentence is input into a CBOW model to obtain a pre-trained part-of-speech vector, so as to learn the fixed collocation information between words, such as "suffered an injury" as a "verb + adverb + noun" structure. Then, the part-of-speech vector, the word vector and the character vector are used to extract the word-level information and the character-level information of the sentence, respectively. When extracting features, a convolutional neural network is used to extract features on the word matrix, the character matrix and the part-of-speech matrix of the sentence. Then, the part-of-speech features are used to calculate the attention score, which is used to assist the model to focus on the verb when calculating; the part-of-speech attention is provided, and after the part-of-speech features are added, the accuracy and efficiency of the model in the trigger word extraction and event type classification tasks are higher.

The invention provides a network request risk detection method and system and a server, and relates to the field of network request risk detection.The method comprises the steps that feature fusion is conducted on an injection keyword matching result, a network request parameter statistical result and a network request character vectorization result corresponding to data to be detected; and after continuous features in the fused feature data are dynamically optimized, accurate detection on the SQL injection risk in the network request process is realized through a lightweight classifier.

This information retrieval system provides an answer corresponding to a question using a large language model. A context information retrieving unit retrieves a document database with a characteristic vector of the question and thereby acquires as context information a text group that a similarity level between the characteristic vector and a characteristic vector of the text group satisfies a predetermined condition. In the document database, character vectors and page numbers of text groups obtained by dividing a document are registered. A prompt generating unit generates a prompt that includes the question and the context information. An answer acquiring unit acquires an answer corresponding to the prompt using a large language model. An answer outputting unit outputs as an answer corresponding to the question the context information and a page number associated with the context information together with the answer corresponding to the prompt.

Systems and methods of computational transliteration of an input sequence of characters in a source language such as Thai to a target language such as Latin. The output may include Romanization of Thai names. The output sequence may be used for machine transliteration understanding of words such as proper nouns. A system may execute a higher-order function that calls a reducing function that iterates through sliding windows of an input sequence and updates, with each iteration, an accumulator map that includes a vector of characters and an indication of a number of characters that can be skipped when processing the next window. Each window includes multiple characters in the input sequence for context-based transliteration using contextual transcription rules. Characters can be skipped when they have already been processed in a previous sliding window. Transliteration of certain source languages may include transposition, deletion, insertion, and transcription.

The application discloses a keyword determination method and device, and relates to the technical field of natural language processing. The specific scheme comprises the following steps: a computer device acquires a text to be processed, performs part-of-speech recognition on each word included in the text to be processed to obtain the part-of-speech of each word, determines position information of each word according to the sequence of each character included in the text to be processed, determines the position information of the part-of-speech of each word according to the position information of each character, then performs keyword recognition on target feature information by using a keyword recognition model to obtain a first keyword set, wherein the target feature information comprises target embedding information and target position information, the target embedding information comprises a character vector of each character and the part-of-speech of each word, and the target position information comprises the position information of each character and the position information of the part-of-speech of each word, and finally, target keywords of the text to be processed are determined. The application can improve the accuracy and recognition rate of keyword extraction.

The process of generating general-purpose information based on text. [Solution] The information processing device according to the present invention comprises an acquisition unit, a vector conversion unit, and an extraction unit. The acquisition unit acquires character vectors converted from characters. The vector conversion unit converts the character vectors into user vectors. The extraction unit extracts target users corresponding to the user vectors.

The application discloses a multi-feature Chinese entity relation extraction method based on deep learning, and particularly relates to the technical field of natural language processing, and comprises the following steps: completing multi-feature Chinese word embedding, using BERT to learn character vector, splicing part-of-speech tags and character information position information as word embedding vector input, and sending the word embedding vector input to a multi-feature recurrent convolutional network, the neural network contains Chinese sentence-level features and character-level features, and after a maximum pooling layer, the features are sent to a softmax classifier as final classification vectors; for each sentence, the category corresponding to the value of the maximum probability is the classification result. The application is suitable for Chinese text relation extraction and can effectively deal with the complex relations of Chinese corpus.

The application discloses a kind of based on disease Chinese explanation cause of death chain detection model training and detection method, training method includes: collecting all ICD coding and Chinese explanation;Obtain cause of death chain sample;Randomly initialize the character vector corresponding to each Chinese character;Chinese explanation of each ICD coding in sample is replaced by character vector, and the corresponding ICD vector is obtained;Each ICD coding in each sample is replaced by ICD vector, and the corresponding vector matrix is obtained;Randomly initialize mask vector, randomly select the row vector in the vector matrix of sample, replace row vector with mask vector, obtain the mask matrix corresponding to each sample;Cause of death chain detection model is constructed, the mask matrix corresponding to each sample is input into model, and the probability that each ICD coding is located in each order in each sample is output;According to the probability that each ICD coding is located in each order in each sample, the total loss of all samples is calculated;Based on total loss, the model is trained, when total loss converges, the training completed cause of death chain detection model is obtained.

The application discloses a movie story generation method and system fusing movie character features, and comprises the following steps: constructing a movie story resource library, respectively adopting a movie character set and a normal distribution to randomly initialize movie character vector representation and movie character verb vector representation; adopting a second adaptive feature to train the movie character verb vector representation, and combining a first and a third adaptive feature, a behavior predictor, an encoder, a CVAE model and a decoder to jointly train the movie character vector representation to calculate the last distribution of words; and determining the content of movie story generation through the last distribution of words, so as to realize the creation of a movie story around a given movie character. The movie story generated in combination with the movie character features can obviously capture movie character information and strengthen the connection between a movie plot and movie characters, so that the explainability and consistency are improved, and the creation of a movie story around a given movie character is realized.

The application provides a Chinese speech synthesis method and device, a terminal and a storage medium. The method comprises: obtaining a Chinese sentence, and performing sub-character recognition on the Chinese sentence to obtain a sub-character sequence; each sub-character in the sub-character sequence contains the meaning of a corresponding Chinese character; performing phoneme conversion on the sub-character sequence to obtain a phoneme sequence and a phoneme position sequence; performing word embedding processing on the sub-character sequence to obtain a sub-character vector; inputting the phoneme sequence, the phoneme position sequence and the sub-character vector into a trained Chinese speech synthesis model to obtain a mel spectrum corresponding to the Chinese sentence; and the mel spectrum is used to synthesize speech corresponding to the Chinese sentence. The application can reduce the difficulty of learning pronunciation rules of a Chinese speech synthesis model, and improve the speed and quality of speech synthesis.

This invention provides a computer implementation method, program, device, and system for training a machine learning model for text generation. [Solution] The method includes inputting text into a machine learning model, preprocessing the input text to obtain multiple character vector representations, encoding each of the multiple character vector representations using an encoder to obtain multiple word vector representations, generating multiple predictive word vector representations based on the multiple word vector representations using a backbone model, decoding the multiple predictive word vector representations using a decoder to obtain multiple character probabilities, and updating the machine learning model based on the multiple character probabilities.