77 results about "Word representation" patented technology

This invention relates to the field of text processing technology, and in particular to a method, device, and medium for processing navigational notice text based on semantic enhancement. The method includes: first, acquiring a content carrier to be processed; then, acquiring the semantic vector and glyph feature vector of the content carrier; concatenating the two types of vectors to form an enhanced text representation; extracting temporal features from the enhanced text representation to obtain temporal features containing forward and backward logical relationships within the text; acquiring the weights of words and sentences in the temporal features and performing weighting to obtain weighted word representations and weighted sentence representations; performing correction processing on the weighted representations to generate corrected text; and finally, validating the corrected text and outputting the target text. This invention can improve the accuracy and efficiency of content carrier processing.

The invention discloses an equipment health state prediction question-answering system and method for pre-training a large language model. The system comprises an acquisition module, a marking module, an editing module, a prediction module and an interaction module. The acquisition module is used for acquiring time sequence data of industrial production equipment and preprocessing the time sequence data to obtain processed data; the marking module is used for dividing and marking the processed data to obtain a marked sequence; the editing module is used for reprogramming the mark sequence to obtain word representation of the time sequence data; the prediction module is used for collecting the health state of the industrial production equipment for prediction based on the word representation to obtain a prediction result; and the interaction module is used for interacting with the user to complete question and answer communication of the prediction result. According to the method, a large amount of structured and unstructured data can be processed, so that the key indexes and modes of the health state of the equipment are identified more accurately, the health state of the equipment is predicted, instant early warning information is provided, and an enterprise is helped to take measures before a fault occurs.

Embodiments of the present disclosure provide a method, an apparatus and an electronic device for determining a word representation vector, and a computer-readable storage medium, which belong to a field of processing natural languages. The method includes obtaining a set of glyph units of a text; obtaining a context vector of the text based on the set of glyph units; and predicting next word of the text based on the context vector. The method for determining the word representation vector of the present disclosure can effectively obtain a corresponding set of glyph units even for hieroglyphics in which hyperbolic characters are prone to appear or languages evolved from the hieroglyphics, thereby improving an accuracy of determining the word representation vector.

The invention discloses a tree species identification method based on visual word bag representation and state space modeling, and relates to the technical field of tree species cross section microscopic image identification, and the method is characterized in that the method comprises the following steps: S1, data set construction; s2, extracting local features by using a visual word bag model; s3, extracting global features by using the state space model; s4, performing multi-level feature fusion; s5, data oversampling and classifier training are carried out; and S6, identifying tree species. The technical problem to be solved by the invention is to provide a tree species identification method based on visual word bag representation and state space modeling, a state space model is introduced to model a feature sequence of a tree species image, a long-term dependency relationship of the feature sequence is mined, and global feature information is extracted. Through fusion of local and global features, multi-level feature representation with strong discrimination capability is constructed. Samples are balanced by adopting a synthetic minority class oversampling technology so as to improve the discrimination capability of a support vector machine classifier.

The invention relates to the technical field of text processing, in particular to a navigation announcement text processing method and device based on semantic enhancement and a medium, and the method comprises the steps: firstly obtaining a to-be-processed content carrier; then obtaining a semantic vector and a font feature vector of a to-be-processed content carrier; splicing the two types of vectors to form enhanced text representation; performing time sequence feature extraction on the enhanced text representation to obtain a time sequence feature containing a forward and backward logic relationship of the text; obtaining weights of words and sentences in the time sequence features and completing weighting to obtain weighted word representation and weighted sentence representation; performing correction processing on the weighted representation to generate a corrected text; and finally, checking the corrected text, and outputting a target text. According to the invention, the processing accuracy and efficiency of the content carrier can be improved.

The present disclosure relates to a text representation method, a word representation method, a corresponding device, a medium and equipment, the text representation method comprising: obtaining a text and performing word segmentation on the text to obtain a word sequence; generating a text vector of the text, and performing at least one round of iteration on the text vector, each round of iteration comprising multiple sub-iterations; in the i-th sub-iteration of each round of iteration, based on a sliding window with a length of k, after sliding the sliding window of the last sub-iteration on the word sequence backward, performing weighted combination on the real word vector of the k words in the current sliding window and the text vector optimized in the last sub-iteration, taking the vector after the weighted combination as the predicted word vector of the next word outside the current sliding window; optimizing the text vector according to the predicted word vector and the real word vector of the next word; and after completing the at least one round of iteration, obtaining a final text vector for representing the text. The present disclosure can accurately obtain the vector representation of the text.

The present application provides a method, apparatus, electronic device, computer-readable medium and computer program product for processing multimodal data. The method of the present application includes: obtaining a text feature vector by performing word representation processing on the text content of the data to be processed; if the data to be processed contains a predetermined number of pictures, obtaining a picture feature vector by extracting the picture features of each picture and mapping the picture features to the text feature space; if the number of pictures in the data to be processed is less than the predetermined number, then after mapping the picture features to the text feature space, the mapping result is zero-filled according to the preset input sequence length; the multimodal fusion features obtained by feature fusion of the text feature vector and the picture feature vector are used as the input of the multimodal model; the multimodal model is trained to output the prediction result of the predetermined prediction task in the multimodal model based on the input multimodal fusion features. The present application realizes the use of one model to simultaneously process pure text data and data containing text and pictures, saving computing resources.

An object of the present invention is to propose a technique that enables even a person who is not an expert to easily identify a DF in order to support creation of a scenario in accordance with an actual situation of an organization such as a company.According to the present invention, there is provided a strategic planning support device 3 that supports strategic planning of a company, the device 3 including: a relationship analysis unit 34 configured to analyze a word representing human behavior or regulations related to each of a plurality of detailed items in a financial index of a predetermined organization, the financial index varying due to a past similar event similar to an event of the predetermined organization; and an output unit 35 configured to output, as each element of a coordinate axis of a driving force, a first word representing human behavior related to a first detailed item having the largest ratio in the financial index and a second word representing human behavior related to a second detailed item in which an absolute value of a correlation coefficient related to a correlation with the human behavior or regulations related to the first detailed item is equal to or less than a threshold value, among the plurality of detailed items.

The embodiment of the application provides a kind of multi-modal large model image segmentation method and device based on hierarchical word representation, mask image is encoded into word sequence by designing mask marker, and progressive generation from shape prototype to local detail is realized by causal attention mechanism.Three-stage training strategy is adopted, first, the marker is trained through the mask reconstruction task, then the mask word is integrated into the large multi-modal model and jointly trained, finally, it is fine-tuned using high-resolution data.Multi-level supervision is carried out based on hierarchical mask loss function, and accurate segmentation of natural language description target is realized.The method effectively solves the deficiencies of traditional technology in complex scene understanding, model training and mask generation, and significantly improves the performance of multi-modal image segmentation.

The application discloses a Chinese named entity recognition method based on pinyin enhancement, and the method comprises the following steps: obtaining a two-dimensional pinyin grid embedding vector and a word representation vector of a text to be recognized; projecting based on the word representation vector to obtain a two-dimensional grid feature vector; calculating a fusion feature between any two characters of the text to be recognized based on the pinyin grid embedding vector and the two-dimensional grid feature vector; calculating a first corresponding prediction score vector based on the fusion feature between any two characters by fusing the pinyin grid embedding vector and the two-dimensional grid feature vector; calculating a second corresponding prediction score vector based on the word representation vector; calculating a total prediction score vector based on the first corresponding prediction score vector and the second corresponding prediction score vector; and outputting the named entity of the text to be recognized based on the total prediction score vector, so that the efficiency of the Chinese named entity recognition based on pinyin enhancement is improved.

The application discloses a kind of multi-modal aspect-level sentiment analysis method, device, equipment and storage medium.The application includes: obtaining multi-modal input data;The multi-modal input data includes input sentence and input image;The input image is input into pre-training conversion model, and the image caption of the input image is output;The context text representation of the input sentence and the context image caption description representation of the image caption are generated;Based on attention mechanism, the context text representation and the context image caption description representation are used to generate semantic information;The syntax mask matrix is constructed using the semantic information;Aspect word representation is obtained by performing graph convolution operation on the syntax mask matrix;The aspect word representation includes text representation and image representation;The text representation and image representation are interactively predicted to obtain the sentiment classification of the multi-modal input data.

The invention belongs to the field of computer vision, and relates to a semantic disambiguity-based image-text retrieval method, a storage medium and a semantic disambiguity-based image-text retrieval device, which comprises the following steps of: obtaining a to-be-retrieved text word, and encoding text data by adopting a Bi-GRU model and a Bert model; carrying out ambiguous word updating on the encoded text by adopting a GAT network; calculating the significance similarity between the updated ambiguous words by adopting a related layer, and generating weight distribution of perceptual significance according to the significance similarity; embedding each ambiguous word into the original text according to the weight distribution to obtain a new ambiguous word expression; aligning the new ambiguous word expression with the image region features; matching the aligned words and images; according to the method, an image-text alignment perception mechanism is designed, and joint optimization of semantic disambiguation and image-text retrieval is achieved. Experiments show that the method significantly improves the accuracy, robustness and practicability of cross-modal image-text retrieval.

Systems and methods for incorporating machine learning to predict post operative right ventricular failure using echocardiograms are described. In an embodiment, the system obtains echocardiography video data describing a patient's heart, generates several dense trajectory descriptors based on the echocardiography video data, reduces the dense trajectory descriptors to a bag-of-words representation, generates a first prediction metric of RV failure based on the bag-of-words representation, generates a second prediction metric based on the echocardiography video data using a neural network, and generates an output prediction metric by applying a weighted classifier to the first prediction metric and the second prediction metric.

The invention discloses a dialogue aspect-based emotional tetrad extraction method, which comprises the following steps of: 1) extracting context-aware word representation for each utterance in a dialogue D; 2) carrying out overall modeling on the semantics of the whole dialogue, and refining the incidence relation between the utterances; 3) performing cross-utterance grammar analysis on a word level; 4) fusing multi-level associated information; and 5) based on the word level representation obtained in the step 4), processing all possible word pairs in the dialogue to obtain a complete emotion tetrad. According to the multi-level association optimization network framework, the association expression of the utterance level is remarkably enhanced through overall semantic modeling, meanwhile, by means of cross-utterance grammar analysis, the association modeling capacity of the word level is effectively improved, and the accuracy of overall tetrad extraction is improved.

Embodiments of the present application provide a method and device for determining word representation vectors, electronic equipment and computer readable storage medium, and belong to the field of natural language processing. The method comprises: obtaining a set of word units of a text; obtaining a context vector of the text based on the set of word units; and predicting a next word of the text based on the context vector. The method for determining word representation vectors can effectively obtain a corresponding set of word units even for pictographic characters or languages evolved from pictographic characters that are prone to out-of-vocabulary words, thereby improving the accuracy of determining word representation vectors.

Embodiments of the present application provide an information retrieval method and device, electronic equipment and computer readable storage medium, and relate to the field of artificial intelligence. The method comprises: dividing a question to obtain at least one question word, and dividing a target document containing an answer to obtain at least one document word; determining at least one document word representation corresponding to at least one document word based on at least one question word and at least one document word; dividing the target document into at least one short sentence, and determining at least one short sentence representation corresponding to at least one short sentence based on at least one document word representation; and determining at least one target short sentence from at least one short sentence as an answer corresponding to the question based on at least one short sentence representation. The present application solves the problems of incomplete semantics and missing extraction in the prior art when extracting long answers, thereby saving the time cost of user information retrieval and improving the user's retrieval experience.

The invention discloses a training method of a cue word quality evaluation model and a quality evaluation method.The training method comprises the steps that a cue word training text is obtained, text sequence word segmentation is conducted on the cue word training text, and a semantic accuracy sequence, an expression integrity sequence and a logic continuity sequence of the cue word training text are obtained; according to the semantic accuracy sequence, the expression integrity sequence and the logic continuity sequence, performing multi-dimensional parallel coding fusion processing to obtain a target cue word representation vector; performing double-branch prediction fusion on the target cue word representation vector to obtain fused prediction data; and according to the fusion prediction data, performing parameter updating on the initialized cue word quality evaluation model to obtain a trained cue word quality evaluation model. According to the training method, a cue word quality evaluation model can be provided, and the model can be beneficial to improving the interpretability and accuracy of cue word quality evaluation. The invention relates to the technical field of natural language processing.

The application discloses a kind of aspect-level sentiment analysis method based on multilevel knowledge enhancement, belong to sentiment analysis field. Including the following steps: S1: text word vector is obtained using GloVe word embedding tool, the syntax dependency tree of sentence is constructed using Stanza, and the dependency graph is constructed accordingly;S2: the context representation of sentence is extracted by inputting word vector into BiLSTM, and the dependency graph is updated using sentiment dictionary and sensitive relationship set, to realize the sentiment and syntax enhancement of sentence;S3: the enhanced dependency graph is input into GCN to model node features, to obtain specific aspect representation;S4: aspect word is enhanced using concept atlas to obtain aspect word representation, which is fused with specific aspect representation to obtain aspect representation;S5: aspect representation and context representation are coordinated and optimized using interactive attention, to obtain the final representation of sentence, so as to determine aspect sentiment tendency.The application can effectively improve the accuracy of specific aspect sentiment classification, and help merchants accurately locate problems in products or services.

The present application relates to a kind of large model atlas question answering method and device based on potential unit fine-tuning, the method includes the following steps: based on the knowledge graph of symbolization and relationship path and the variable of using high-dimensional prompt word representation potential unit, potential relationship learning is carried out between large language model and knowledge graph, and relationship reasoning path is generated, wherein the input layer of large language model is additionally trainable potential unit;And potential unit is randomly initialized by normal distribution, and is fine-tuned with large language model in hidden learning process;Relevance evaluation is carried out to relationship reasoning path, and the most relevant relationship reasoning path is selected;Input natural language question, and the answer in knowledge graph is obtained by the most relevant relationship reasoning path.The present application filters out irrelevant and misleading context fragments using these fine-tuned potential units, while improving response efficiency and performance.

The invention discloses a lexical element representation method and device and electronic equipment. The method comprises the following steps: S1, performing word segmentation on an input text by adopting a first word segmentation device to obtain a first lexical element sequence arranged according to the sequence of the input text; s2, performing word segmentation on the input text by adopting a second word segmentation device to obtain a second lexical element sequence arranged according to the sequence of the input text; extracting lexical units, different from the first lexical unit sequence, in the second lexical unit sequence to form a combined phrase set; s3, converting the lexical elements corresponding to the first lexical element sequence into a first embedded sequence, converting the lexical elements corresponding to the phrases in the combined phrase set into embedded vectors, and fusing the embedded vectors into a second embedded sequence; and replacing the corresponding elements in the first embedding sequence with the elements in the second embedding sequence to obtain a compressed embedding sequence. On the premise that an original model embedding matrix can be reused, high-frequency cross-word boundary phrases are dynamically fused to shorten the sequence length, and therefore the calculation efficiency and the expression capacity of a subsequent model are improved.

Embodiments of the present invention relate to an intelligent question-answering method, device, equipment and storage medium, which use two context-aware transformations to incorporate context information into the word representation in the dialogue scenario; store the dialogue scenario and the knowledge base semantic information separately; understand the user's current statement through a memory network, and activate the current dialogue generation task; the working memory dynamically controls the "activation" of the long-term memory, reads the content related to the current task from the long-term memory into the short-term storage, and then generates the current reply word by word based on a heuristic strategy. The present invention can store the dialogue scenario and the knowledge base semantic information separately, and utilize the information stored in these two ways through memory reasoning, can efficiently utilize the external knowledge base information, and greatly improves the performance of task-based dialogue generation.

The invention discloses a named entity recognition system and method based on parallel LSTM (Long Short Term Memory) and a gating mechanism, and relates to the technical field of computer data processing, and the system comprises a character modeling module which is used for generating character-level word representation from an input character sequence; the statement modeling module is used for splicing the character-level word representation and a word-level pre-training representation searched from an external pre-training word vector library according to a word index to obtain a word representation matrix, and obtaining a statement-level word representation based on the word representation matrix; and the sequence decoding module is used for performing sequence-level labeling decoding on the statement-level word representation and outputting an entity label sequence. The method can reduce redundant calculation and stabilize reasoning.

The invention discloses a Chinese named entity recognition method based on pinyin enhancement. The Chinese named entity recognition method based on pinyin enhancement comprises the steps of obtaining a two-dimensional pinyin grid embedding vector and a word representation vector of a to-be-recognized text; performing projection based on the word representation vector to obtain a two-dimensional grid feature vector; based on the pinyin grid embedding vector and the two-dimensional grid feature vector, calculating a fusion feature between every two arbitrary characters of the to-be-recognized text; the method comprises the following steps: by fusing a pinyin grid embedding vector and a two-dimensional grid feature vector, calculating a first corresponding prediction score vector based on a fusion feature between every two arbitrary characters; calculating a second corresponding prediction score vector based on the word representation vector; calculating a total prediction score vector based on the first corresponding prediction score vector and the second corresponding prediction score vector; and outputting the named entity of the to-be-recognized text based on the total prediction score vector. The Chinese named entity recognition efficiency based on pinyin enhancement is improved.

The invention discloses a word sense disambiguation method based on quasi-quantum non-homologous representation constraint. The method comprises the following steps: obtaining target word representation corresponding to a target word in a to-be-disambiguated text; obtaining word meaning definition representation and word meaning example representation in the dictionary; based on the target word representation and the word meaning definition representation, respectively calculating similarity scores of the target word representation and each word meaning representation, namely traditional word meaning recognition scores; calculating a similarity score of the target word representation and the word meaning representation obtained under each non-homologous representation constraint mechanism, namely a quantum word meaning recognition score; and adding the results of the traditional word meaning recognition score and the quantum word meaning recognition score to serve as a word meaning recognition result finally output by the word meaning disambiguation model.

The application discloses a patent technology prediction system and method fusing a time sequence knowledge graph and contrast learning, relates to the patent technology prediction field, and is proposed in view of the problem of inaccurate technology prediction in the prior art. Patent elements are extracted and standardized; quadruples are constructed and heterogeneous relationships are defined; time slicing is performed and a graph snapshot sequence is generated; structural flow and heterogeneous graph structures are encoded respectively; time flow is encoded; patent semantics are represented and a unified space is aligned; patent representation and real associated technology keyword representation are explicitly semantically aligned based on a contrast learning enhancement mechanism; coarse retrieval results are obtained; fine retrieval results are obtained and rearranged; coarse ranking scores and fine correction scores are fused and output; multi-level ranking targets are jointly designed; a rearranger and a Gold Injection strategy are trained; and an overall loss function and end-to-end training are performed. The application has the advantages of being capable of depicting the dynamic evolution process of patent technology association changing over time, improving the overall quality of prediction results, and the like.

This invention provides a question error correction method, apparatus, electronic device, and storage medium. It employs a question error correction model, which includes an encoding layer and a decoding layer. The encoding layer comprises a first-type convolutional neural network layer and the encoding end of a Transformer layer. The decoding layer comprises a second-type convolutional neural network layer and the decoding end of a Transformer layer. The question text samples and their corresponding corrected question text samples used during training can be selected based on the required accuracy of the question error correction model, thus addressing the problem of excessively large parameter space in N-gram models when N is too large. Furthermore, since the question error correction model utilizes word vectors, which are a distributed feature representation, they take into account the semantics of words, effectively mitigating the high sparsity of traditional statistical word representations.

The present invention proposes a method for learning character and word representations driven by dual self-attention, which includes: inputting text and a given passage, performing syntactic and semantic analysis on the passage, and extracting multi-level features in combination with a pre-trained language model; using a feature fusion module to capture the span correlation of the pattern semantics of characters and the context semantics of words for different semantic embeddings; using a bidirectional self-attention mechanism to calculate the attention weights of character-word and word-character, and updating the initial embedding representations of characters and the initial embeddings of words; performing interactive iteration on the updated character embedding representations and the updated word embedding representations; inputting the final character embedding representations into a multi-layer perceptron for fusion and update, and using a span predictor for prediction to obtain a prediction result. The present invention designs a model and a system that integrate an embedding learning framework and an embedding learning method, and trains and learns effective character and word representations.

The present invention discloses a tree species identification method based on bag-of-visual-words representation and state-space modeling, which relates to the technical field of tree species cross-section microscopic image recognition and is characterized in that it includes the following steps: S1: data set construction; S2: local feature extraction using a bag-of-visual-words model; S3: global feature extraction using a state-space model; S4: multi-level feature fusion; S5: data oversampling and classifier training; S6: tree species identification. The technical problem to be solved by the present invention is to provide a tree species identification method based on bag-of-visual-words representation and state-space modeling, which introduces a state-space model to model the feature sequence of tree species images, mines their long-term dependencies, and extracts global feature information. By fusing local and global features, a multi-level feature representation with strong discriminative ability is constructed. The synthetic minority class oversampling technology is used to balance the samples to improve the discriminative ability of the support vector machine classifier.

The present invention discloses a method for determining vocabulary relevance based on subject information and sub-domain prior knowledge, the method comprising: obtaining a material text, a material text title, and a sub-domain material text, and processing the material text, the material text title, and the sub-domain material text; determining preliminary word representation data of scientific vocabulary in the material text and the material text title based on the processed material text and the material text title; determining representation data of keywords in the sub-domain material text based on the processed sub-domain material text; determining cosine similarity between scientific vocabulary based on the representation data of keywords and the preliminary word representation data, and determining the relevance between scientific vocabulary based on the cosine similarity. The present invention introduces text information rich in sub-domain prior knowledge into the word representation layer, enhances the ability of word vectors to represent scientific vocabulary in specific fields, screens scientific vocabulary based on similarity, and accurately obtains the relevance between scientific vocabulary.

The invention provides a cue word generation model, an optimization method thereof and a cue word generation method, belongs to the field of artificial intelligence, and can solve the problem that an existing cue word generation model is poor in cue word optimization effect. The optimization method comprises the steps of training an initial cue word generation model based on a first training data set to obtain a first cue word generation model; the first training data set comprises a plurality of first samples, and the first samples comprise original cue words and initial optimization cue words; training the first cue word generation model based on the second training data set to obtain a second cue word generation model; the second training data set comprises a plurality of second samples, and the second samples comprise the first type of original cue words and corresponding preference cue words and non-preference cue words; the preference cue words and the non-preference cue words are obtained based on a first cue word generation model; the first type of original cue words represent cue words used in combination with the first type of task information, and the first type of task information refers to task information with standard answers.