12 results about "Word graph" patented technology

1. The name of the design product: electronic device's word book word list scene word graph user interface. 2. The use of the design product: for an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: front view. 5. The product carrier is the conventional design, and the rear view, left view, right view, top view, and bottom view are omitted. 6. The use of the graphical user interface: for the user to switch 10 scene word books, and manage the words of each word book through the word list. 7. The human-computer interaction mode of the graphical user interface: the front view is the initial interface. Click the expand word list details button in the front view to enter the interface change state diagram.

The invention relates to the technical field of generalization encryption, and discloses a generalization processing method and system for text data in economic big data, and the method comprises the steps: firstly carrying out the standardization and word segmentation of original text data, and carrying out the statistics of a word pair co-occurrence relation through a preset sliding window parameter; then, according to co-occurrence counting, the correlation strength of word pairs is calculated and is not negative, and a weighted word graph with the correlation strength as the edge weight is constructed; calculating a normalized graph matrix and performing feature analysis to obtain a second feature value and a second feature vector, thereby calculating an edge-level sensitive value and determining word-level sensitivity; screening a high-sensitivity word set according to a threshold parameter, generating a window radius in a feature vector coordinate space according to a coordinate difference between a high-sensitivity word and a peak-causing neighbor node and a radius parameter, forming a word bucket, and combining overlapped intervals; and finally, generating bucket codes for each word bucket by adopting an encryption algorithm, replacing words in the buckets, and outputting generalization encrypted text data with privacy protection and structure availability.

This invention relates to a method for identifying hot words in customer conversations based on large-scale model technology. The identification method is as follows: model training and construction; users upload batches of historical conversation texts or access conversation streams in real time through the system interface; the system automatically segments the input text into sentences, labels roles, and unifies the encoding format; the trained model processes the preprocessed conversation text; conversation summaries are generated based on hot word graphs; and the results are visualized and output. Compared with existing technologies, this invention achieves accurate identification and classification of the deep meaning of hot words through a multi-dimensional tagging system and semantic understanding; it couples hot word identification and summary generation through multi-task learning and semantic graphs, making the generated summaries both focused and coherent; it supports automated and intelligent analysis of massive conversation data, greatly improving the efficiency and depth of customer sentiment analysis, and providing direct data support for product optimization, service improvement, and risk warning.

This natural language processing device comprises: a first input unit; a second input unit; a first word extraction unit; a second word extraction unit; a correspondence relationship determination unit; and an output unit. The first word extraction unit receives an input of a first target, which includes a natural language description. The second word extraction unit receives an input of a second target, which includes a natural language description. The first word extraction unit extracts at least one first word from the first target. The second word extraction unit extracts at least one second word from the second target. The correspondence relationship determination unit determines the presence or absence of a relationship between the first word and the second word. The output unit performs output processing on the basis of the results of determining the presence or absence of a relationship. The correspondence relationship determination unit: uses the first word as a start node; searches for, from the start node, a word graph in which at least one complementary word related to the first word is connected by an edge indicating a relationship between the first word and the complementary word; and determines the presence or absence of a relationship between the first word and the second word on the basis of whether it is possible to trace back to the second word.

The invention relates to the technical field of voice recognition, and discloses a medical term recognition method, device and equipment and a medium, and the method comprises the steps: obtaining mixed voice data; performing language labeling on the mixed voice data, and determining language boundary tags of Cantonese, Mandarin and English voice segments contained in the mixed voice data; running special speech recognition engines corresponding to Cantonese, Mandarin and English in parallel, decoding the mixed speech data, and generating candidate word graphs of three languages; performing cross-language alignment on the candidate word graph of the three languages to obtain an aligned word graph; recalling the candidate medical terms from the aligned word graph to obtain a candidate medical term list; and screening final medical terms from the candidate medical term list by using a pre-trained language model. According to the method, the canestrun mixed speech in the same sentence is effectively processed, the recognition problem caused by language mixing and semantic ambiguity is solved, and the recognition accuracy of the medical terms is remarkably improved.

The technical solution utilizes an LLM based on a GNN model to provide multilingual and multi-dialect text processing. The system can receive a query in a first language, the query corresponding to a wordpiece. The DPS can generate a first vector for a first wordpiece in response to inputting the wordpiece into a GNN trained based on a corpus, the wordpiece corresponding to a word graph that indicates associations between wordpieces in multiple languages. The DPS can generate a second vector for a second wordpiece in response to inputting the wordpiece into an LLM trained based on a plurality of interactions. The DPS can establish a priority for one of the first wordpiece or the second wordpiece based on at least a first weight of the first vector and a second weight of the second vector, and provide a response to the query using the first wordpiece or the second wordpiece according to the priority.

The long document classification method and device based on hierarchical multi-granularity interactive graph convolution network can construct a network to depict complete hierarchical structured information of a long document and perform information interaction between graphs under the condition of controlling model calculation complexity.The method comprises the following steps: (1) obtaining hierarchical multi-granularity representation of a long document; (2) performing multi-layer hierarchical superimposed paragraph graph convolution, sentence graph convolution and word graph convolution and corresponding inter-graph interaction; (3) in order to fuse semantic information of different granularities and different scales, maximum pooling is used to respectively aggregate the final layer output of the paragraph graph and the output of each layer of the sentence graph and the word graph.

The application relates to an artificial intelligence technology and discloses a text keyword association method, which comprises the following steps: adding a plurality of semantic feature layers in a pre-constructed basic vector conversion network to obtain an original vector conversion model; performing model training on the original vector conversion model by using a business text data set to obtain a standard vector conversion model; extracting a candidate association word set based on the word frequency and point mutual information value of the words in the text to be associated; performing vector conversion on the candidate association word set by using the standard vector conversion model to obtain an association word vector set; performing association word association on a target keyword in the candidate association word set based on the similarity of each vector to obtain an association word graph. The application also relates to a blockchain technology, and the association word graph can be stored in a node of the blockchain. The application further provides a text keyword association device, an electronic device and a readable storage medium. The application can improve the accuracy of text keyword association.

The invention relates to the technical field of data processing, and provides a digital archive classification recording method and system convenient for query and search, and the method comprises the steps: collecting a plurality of to-be-processed archives, and extracting the abstract and original text of each to-be-processed archive; extracting a plurality of effective words from the abstract of the to-be-processed file, and screening core words of a plurality of abstracts; further screening to obtain core associated words of the abstract, and obtaining supplementary words of the original text to form candidate words of the file to be processed; constructing a word graph to obtain an initial score of each candidate word; iteratively updating the score of each candidate word in the word graph to obtain a final score of each candidate word; obtaining a plurality of theme clusters of the to-be-processed file; obtaining a top-layer core word and a plurality of sub-tag core words of each topic cluster; constructing a tree structure; and according to the tree structure, the to-be-processed file is concluded and recorded. The method aims at solving the problem that traditional digital archive classification depends on fixed dimension division, so that the multi-dimension searching efficiency is influenced during searching.

This invention relates to the field of language processing technology, and also to the field of artificial intelligence technology, particularly to a text correction method, apparatus, electronic device, and storage medium. The method involves acquiring text to be processed, inputting the text into a feature encoding model to obtain a first feature vector, inputting the first feature vector into a text error type classification model to output a first probability value, a second probability value, and a third probability value, inputting the first feature vector into a first graph convolutional neural network model based on a homophone graph network to obtain a second feature vector, inputting the first feature vector into a second graph convolutional neural network model based on a similar-looking character graph network to obtain a third feature vector, weighting the first, second, and third feature vectors to obtain a fused feature vector, and inputting the fused feature vector into a transformer model to obtain the correction result. This method solves the technical problem of homophone and similar-looking character errors in text.

This application provides a speech recognition method, speech recognition device, electronic device, and storage medium, belonging to the field of financial technology. The method includes: acquiring target speech data; extracting acoustic features from the target speech data to obtain target acoustic features; decoding the target speech data based on a decoding model to obtain a target word graph; the target word graph includes word nodes and a speech feature sequence, the speech feature sequence including at least two speech words; constructing multiple candidate sentence paths based on the word nodes and speech feature sequence; initially scoring the speech words of each candidate sentence path to obtain a preliminary word score; correcting the preliminary word score based on the target acoustic features to obtain a target word score; filtering the candidate sentence paths according to the target word score to obtain a target sentence path, and concatenating the speech words of the target sentence path to obtain target sentence data. This application can improve the accuracy of speech recognition.

The invention relates to an ICLextRank dense point word extraction method based on fused semantics. The method comprises the following steps of text preprocessing, word graph construction, multi-feature fusion calculation and dense point word generation. The method comprises the following steps: firstly, performing sentence division, word segmentation, stop word removal and part-of-speech screening on an original document; secondly, constructing a word graph model based on a word co-occurrence relationship; then, the word features, the center features and the semantic features of the candidate words are synthesized for scoring, and text semantic vectors are extracted from the semantic features through a DistilBERT model and calculated through cosine similarity; and finally, sorting the candidate words according to the comprehensive score and outputting Top-K dense point words. According to the method, through combination of semantic fusion and centrality analysis, the accuracy and the intelligent level of dense point extraction are improved, and automatic auxiliary support is provided for electronic document secret setting.