12 results about "Paraphrase" patented technology

The application belongs to the field of natural language processing, and particularly relates to a method and system for paraphrase recognition based on semantic primitive knowledge and abstract semantic representation, which comprises the following steps: performing word segmentation on a sentence, and performing word-level vector representation and semantic primitive knowledge representation; performing mean value processing on the semantic primitive knowledge representation result, and extracting interactive attention feature information of the mean value processing result by using global semantic information to obtain global semantic primitive representation; performing abstract semantic analysis on a to-be-recognized paraphrase sentence from a sentence structure to obtain a single-root directed acyclic graph, and performing global semantic primitive representation and word-level vector representation; extracting global and local feature information in the order of the directed acyclic graph, and performing distance feature measurement on the information; inputting the distance feature measurement result into a neural network to obtain a recognition result; the application introduces external semantic primitive knowledge to perform semantic representation, the accuracy of the semantic primitive knowledge representation is assisted by global semantic information, and the abstract semantics of a Chinese paraphrase sentence is analyzed to obtain semantic relations, so that the accuracy of paraphrase recognition is improved.

The invention relates to the technical field of vocabulary memory training, and discloses a vocabulary memory training method and system based on handwriting interaction, and the system comprises a dictation module which is configured to enter a handwriting training environment after determining a to-be-memorized vocabulary, play the voice of the to-be-memorized vocabulary and display the paraphrase of the to-be-memorized vocabulary, and then carry out the handwriting input of a user; the acquisition module is configured to determine a next-round initial training interval based on the analysis result; the judging module is configured to judge whether to adjust the initial training interval of the next round according to the writing characteristic parameters; the processing module is configured to determine an adjustment coefficient of a next-round initial training interval based on the historical training parameters and obtain a next-round final training interval; and the execution module is configured to train the vocabularies to be memorized at the following final training interval. According to the method, the next training interval can be scientifically and reasonably determined according to the handwriting input result of the user, the writing characteristic parameters and the historical training parameters of the vocabularies to be memorized, and personalized vocabulary memorizing training is achieved.

A computing system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to: receive a paraphrase comprising a paraphrase text sample corresponding to an original text sample; and calculate a paraphrase metric value corresponding to the paraphrase, wherein the paraphrase metric value is calculated based on an adequacy score, a novelty score, and a fluency score of the paraphrase, the adequacy score indicating an extent to which the paraphrase text sample preserves a meaning of the original text sample, the novelty score indicating a level of difference between words and characters of the paraphrase text sample and words and characters of the original text sample, and the fluency score indicating an extent to which the paraphrase text sample is devoid of repetition, spelling, and grammatical mistakes.

The invention discloses a book reading method and system based on artificial intelligence, a medium and a product, and relates to the field of data processing. In the method, a book text is segmented into a plurality of text sentence segments based on preset punctuation marks; performing text type judgment on each text sentence segment, and dividing the text sentence segment into a paraphrase text and a role dialogue text; performing sentiment analysis on the parastyle text and the role dialogue text to generate a corresponding parastyle sentiment identifier and a corresponding role sentiment identifier; calling a first speech synthesis strategy to perform speech synthesis on the paraphrase text according to the paraphrase emotion identifier to obtain a paraphrase speech segment, and calling a second speech synthesis strategy to perform speech synthesis on the role dialogue text according to the role emotion identifier to obtain a role speech segment; and according to the original sequence of the text sentence segments in the book text, splicing the white speech segments and the role speech segments to obtain a target leading audio. By implementing the technical scheme provided by the invention, the artificial intelligence reading experience feeling of the user is improved.

The text embedding model training method and the related device provided in the application, the data processing device obtains the paraphrase vector of the target vocabulary and the initial vector of the sample text, inputs the paraphrase vector of the target vocabulary and the initial vector of the sample text into the first neural network model to be trained for training, and obtains a text embedding model having an embedding vector conversion function. Since the sample text includes the target vocabulary, and the paraphrase vector of the target vocabulary is an embedding vector representing the paraphrase information of the target vocabulary, the paraphrase information is used as prior knowledge, so that the trained text embedding model has a better embedding vector conversion effect.

This invention proposes a paraphrase generation method based on a controllable latent space diffusion model, comprising the following steps: Step 1, constructing a paraphrase model based on a latent space diffusion model, and training the paraphrase model based on an existing paraphrase text dataset; the existing paraphrase text dataset contains the original sentence and its paraphrase; Step 2, dividing the paraphrase source sentence into key information and non-key information, and constructing enhanced semantic fragment information; Step 3, constructing a controller; Step 4, training the controller; Step 5, combining the paraphrase model based on a latent space diffusion model trained in Step 1 and the controller trained in Step 4 for collaborative reasoning to generate paraphrased sentences, thus completing the paraphrase generation task based on a controllable latent space diffusion model.

A method of interpreting a verbal input, may include: assigning a meaning classification to the verbal input, and a confidence score to the meaning classification; and based on the confidence score corresponding to the meaning classification of the verbal input being less than or equal to a threshold, generating at least one paraphrase of the verbal input using at least one large language model (LLM); assigning the meaning classification to the at least one paraphrase, and the confidence score to the meaning classification; and concatenating the verbal input, the at least one paraphrase, the meaning classification, and the confidence score to generate a concatenated input; inputting the concatenated input into the at least one LLM.

The present application relates to entity relation annotation and correction method based on semantic understanding, and the technology comprises: obtaining a to-be-processed text, performing sentence division and semantic unit segmentation on the to-be-processed text, identifying entity mention, candidate relation predicate and semantic trigger; generating a relation proposition unit for a candidate entity pair, the relation proposition unit comprising a subject entity, an object entity and a candidate relation type; performing relation establishment determination on the relation proposition unit based on the semantic trigger, and determining a relation state label; when the relation state label represents relation establishment, outputting corresponding structured relation data as an entity relation annotation result; the present application generates a relation proposition unit for a candidate entity pair based on semantic understanding, first judges whether the relation actually exists in the current text context, thereby reducing the probability of false annotation in negative, speculation, paraphrase, conditional sentence and other scenarios, and improving the accuracy and semantic consistency of the entity relation annotation result.

The application discloses a unified low sample relation extraction method and device based on a multiple-choice matching network. The method comprises the following steps: a common coding and matching mechanism based on a pre-training language model and multiple-choice marking relation description and relation instance; a triple obtained through open information extraction of large-scale pure text and a paraphrase text generated through a generative pre-training language model, and a triple-paraphrase pre-training mode based on the same; and an online meta-learning training mode based on a small sample under a new task. The mechanism based on the multiple-choice matching network can model various scenes in the low sample relation extraction task, and provides an efficient and fast network architecture, so that the model is more in line with the multiple requirements of model performance and speed in actual application.

An example operation may include one or more of tuning a language model based on dependencies between an original data set and a paraphrase data set of the original data set, parsing and annotating the paraphrase dataset with entity identifiers of predefined entities to generate an annotated paraphrase dataset, additionally tuning the language model based on entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase dataset, and storing the additionally tuned language model in a storage device.

The application discloses a kind of paraphrase generation method, device, equipment and storage medium, method includes obtaining first paraphrase generation corpus and word segmentation processing, the input word sequence X_1 and label word sequence Y_1 obtained are used as pre-training data set to train neural network model M;Second paraphrase generation corpus is obtained and is constructed knowledge base by neural network model M, so that the paraphrase generation knowledge contained in the second paraphrase generation corpus comprising first paraphrase generation corpus and incremental paraphrase generation corpus with timeliness exists in the form of key-value pair in knowledge base, the input word sequence X_3 obtained by third paraphrase generation corpus word segmentation processing is input into neural network model M to predict, obtain neural network prediction result and query vector;Query vector is used to retrieve knowledge base, and obtain retrieval result;Fusion neural network prediction result and retrieval result, generate final paraphrase text.Knowledge base makes paraphrase system effective iteration update, and generates paraphrase text with decision basis.

The application discloses a kind of based on template sentence enhancement's paraphrase sentence generation method, steps are as follows: step 1: obtain original sentence, and obtain the target paraphrase sentence of original sentence in parallel corpus, use the method of rule to retrieve 1 sentence from parallel predata with the highest similarity with target paraphrase sentence as optimal sentence as reference;Step 2: different part-of-speech special symbols are used to replace noun, verb, adjective and adverb in optimal sentence to construct template sentence;Step 3: original sentence and template sentence are spliced through special symbol and joint, and the spliced sentence is used as the input of paraphrase generation model, and the paraphrase generation model outputs several paraphrase sentences;Step 4: the similarity of several paraphrase sentences and original sentence in grammatical structure and semantics is calculated, and the highest similarity paraphrase sentence is used as the optimal paraphrase sentence of original sentence.The application avoids the information dependence of model training phase in template sentence by masking the words of related part-of-speech in template sentence.