5 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.

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 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.