71 results about "Sentence pair" patented technology

System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

The invention discloses a bidirectional information retrieval enhancement generation method for a large language model, and belongs to the technical field of artificial intelligence. In order to overcome the defects that noise is introduced and key evidences are omitted due to the fact that traditional RAG only executes'query-document 'one-way retrieval, a two-way semantic perception retrieval enhancement generation model and a two-stage training framework are constructed, wherein in the first stage, the positive / negative example distance is increased in an embedded space in a contrast learning self-supervision mode; in the second stage, fine-grained correlation discrimination is carried out on query-document bidirectional sentences through supervised dichotomy, and probabilistic correlation scores are output; in the reasoning stage, the bidirectional probabilities are fused according to Bayesian to obtain final relevancy, document reordering is carried out, and plug and play can be achieved without fine adjustment of LLM in the whole process. According to the method, the accuracy and consistency of single-hop and multi-hop questions and answers and fact checking tasks are remarkably improved, and the method has the advantages of light weight and low deployment cost.

Method and apparatus for dynamic remediation. A set of records associated with a project is accessed. The set of records is processed using one or more natural language processing techniques to generate textual data comprising a plurality of pairs of sentences corresponding to one or more topics associated with the project. An issue for at least one topic associated with the project is identified based on the textual data, comprising identifying a pair of sentences that comprises a first sentence and a second sentence, calculating a sentence similarity score by comparing the first and second sentences using a similarity metric, and determining that the sentence similarity score satisfies one or more criteria. In response to determining that the one or more criteria are satisfied, a project meeting for the issue is scheduled based at least in part on a criticality of the issue.

The application relates to a sentence vector generation method and device, a matching method and device and a storage medium. The sentence vector generation method comprises the following steps: preprocessing a received original sentence to obtain a filtered stop word sentence and a filtered stop word and entity label marked sentence; processing the filtered stop word sentence to obtain a first sentence vector, wherein the first sentence vector comprises attribute information of characters included in the filtered stop word sentence; processing the filtered stop word and entity label marked sentence and the original sentence to obtain a second sentence vector, wherein the second sentence vector comprises information of the entity label; and a target vector of the original sentence comprises the first sentence vector and the second sentence vector. The target vector of the sentence obtained by the method can contain more effective information, which is beneficial to improving the accuracy of the result output by an intelligent question and answer system.

The invention provides a text information recognition method and device, equipment and a storage medium. In some embodiments of the present disclosure, a first paragraph text and a second paragraph text of an audit report text are obtained; performing sentence segmentation processing on the first paragraph text and the second paragraph text to obtain a first sentence of the first paragraph text and a second sentence of the second paragraph text; combining the first clause and the second clause pairwise to obtain a sentence pair; encoding each sentence pair to obtain a sentence vector corresponding to each sentence pair; performing semantic emotion recognition on the sentence vector corresponding to each sentence pair to obtain a consistency result of each sentence pair; determining consistency information of the first paragraph text and the second paragraph text according to a consistency result of each sentence pair; based on semantic emotion recognition in the field of natural language processing, the audit report text is subjected to consistency auditing automatically, the labor cost is reduced, the auditing efficiency is improved, and the auditing accuracy is improved.

The application discloses a bidirectional information retrieval enhancement generation method for a large language model, and belongs to the technical field of artificial intelligence. In order to overcome the defects of traditional RAG that only performs one-way retrieval from 'query to document' and introduces noise and omits key evidence, a bidirectional semantic perception retrieval enhancement generation model and a two-stage training framework are constructed. In the first stage, the positive / negative example distance is pulled apart in the embedding space in a contrast learning self-supervised manner. In the second stage, a supervised two-classification is used to finely distinguish the relevance of the query-document bidirectional sentence pair, and an output probability correlation is obtained. In the reasoning stage, the bidirectional probability is fused according to Bayes to obtain the final correlation degree, and the document is reordered. The whole process can realize plug and play without fine-tuning the LLM. The application significantly improves the accuracy and consistency of single-hop, multi-hop question answering and fact checking tasks, and has the advantages of light weight and low deployment cost.

The application belongs to the field of databases and provides a code conversion method, device, equipment and medium based on statement pair equivalent replacement, which comprises the following steps: obtaining a to-be-converted statement including a first operator and a first logical statement in PLSQL code to be converted, the first operator being used for performing a logical NOT operation, and the logical operation result of the first logical statement being Null; obtaining a target logical operation result of the to-be-converted statement; removing the first operator and converting the first logical statement into a second logical statement with a logical operation result of the target logical operation result; and replacing the second logical statement with the to-be-converted statement to convert the obtained target PLSQL code into target Java code. According to the technical scheme of the embodiment, when the logical operation result of the first logical statement in the to-be-converted statement is Null, the to-be-converted statement can be replaced by the second logical statement in a pair-equivalent manner, so that the operation logic remains unchanged after being converted into a Java statement and the normal operation of the system is ensured.

The invention provides a subtitle processing method and system, electronic equipment and a storage medium, and is applied to the technical field of data processing. A plurality of sentences and original subtitle sentence durations thereof are generated according to a subtitle file; wherein the sentence is composed of at least one subtitle, and the original subtitle sentence duration is the sum of the original subtitle duration of all subtitles forming the sentence; translating the sentences to obtain translations of the sentences; based on the sentences and the duration of the original subtitle sentences, performing minimum deviation segmentation on translations of the sentences to obtain segmentation schemes of the sentences; wherein the segmentation scheme comprises a translation fragment of each subtitle corresponding to the sentence; according to the method and the device, the translation fragment of each subtitle is dubbed, and the dubbing rate of the dubbing of the translation fragment of each subtitle is adjusted based on the original subtitle duration of the subtitle, so that the aim of high-precision time sequence alignment is fulfilled under the conditions of avoiding sentence splitting and semantic incoherence and ensuring semantic integrity and a watching process.

The application provides an aspect-level sentiment analysis method and device based on contrast learning. The method comprises the following steps: S1, generating a plurality of prompt sentence pairs based on a plurality of preset aspect sentiment pairs; S2, inputting a to-be-analyzed sentence into an aspect-level sentiment analysis model based on contrast learning to obtain an analysis result, wherein the aspect-level sentiment analysis model comprises: an enhancement module, which combines the to-be-analyzed sentence with a question or answer prompt sentence in different prompt sentence pairs to obtain different to-be-analyzed enhanced sentences; a pre-training coding layer, which obtains a sentence representation vector and a word vector of the to-be-analyzed enhanced sentence; a first activation function layer, which obtains a first analysis result; and a second activation function layer, which marks the position of a target word matched with the aspect sentiment pair to obtain a marked sequence. The joint detection of the target, the aspect and the sentiment is realized, high-quality semantic information can be generated after the to-be-analyzed sentence is enhanced by using the question or answer prompt sentence, the problem of sparse marked data is effectively alleviated, and the sentiment analysis effect is improved.

This invention relates to a method and system for optimizing machine translation using a large language model, belonging to the field of machine translation technology. It includes: generating a corresponding first translation from a source sentence in a bilingual corpus; segmenting the source sentence into words, counting the frequency of easily misspelled words in each segment, and calculating the easily misspelled word score, obtaining an easily misspelled word set based on the score; calculating the semantic similarity between the sentence to be translated and multiple candidate examples, and calculating the quality scores of the multiple candidate examples; selecting the optimal k candidate examples from the multiple candidate examples based on semantic similarity and quality scores, and constructing them as prompt templates; obtaining training sentence pairs from the bilingual corpus based on the easily misspelled word scores to construct a training set; and using the training set and prompt templates to perform low-rank adaptive training on a large language model to obtain an optimized large language model. This invention not only improves translation quality but also enhances the interpretability of the translation process.

The invention discloses a subject information-based field bilingual sentence pair selection method, which is used for selecting a sentence pair subset related to a to-be-translated text from a large-scale bilingual corpus mixed with fields by virtue of subject correlation between a bilingual sentence pair and a target field so as to train a specific field translation system and improve the translation quality of the field text. The method comprises the following steps: firstly, learning topic vectors of phrase pairs by using context words of the phrase pairs in a bilingual corpus; secondly, for a target domain development set and a candidate bilingual sentence pair, obtaining topic vectors of the target domain development set and the candidate bilingual sentence pair by utilizing the extracted phrase pair set; and finally, the topic relevancy between the candidate bilingual sentence pairs and the field development set text is calculated, and the sentence pairs with high relevancy are preferentially selected as target field training data. The invention further discloses a domain bilingual sentence pair selection system based on the theme information. According to the method, the field-related bilingual sentence pairs are selected by means of the topic relevancy of the text, and the problem of insufficient training data in a specific field is solved.

To solve the problem in which there is a need for a technique which makes it even easier to assign tags to a text.SOLUTION: There is provided a program for operating a computer including a computer processor and configured to manage information of a plurality of tags assigned to a text in a storage unit. The program causes the computer processor to perform the steps of: generating an explanatory text describing the tags by providing a large language model with tag information; acquiring a text to which the tags are to be assigned; identifying tags corresponding to the acquired text on the basis of the acquired text and the generated explanatory text of the tags; and presenting the identified tags to a user in association with the acquired text.SELECTED DRAWING: Figure 12

The application discloses an intelligent substation virtual loop automatic checking method and system, which can be applied to the technical field of intelligent substations, obtains text descriptions of multiple virtual terminals in an intelligent substation virtual loop, then cleans each text description to obtain corresponding cleaned texts, encodes each cleaned text by using a target checking model trained based on an SBERT network model to obtain embedding vectors of each character, obtains corresponding sentence vectors based on the embedding vectors of the characters, determines multiple sentence pairs according to the sentence vectors, calculates the similarity between each sentence pair to obtain a text similarity value, and judges whether the text similarity is greater than a preset threshold value; if yes, each virtual terminal is determined to be matched, and the above method significantly improves the accuracy of automatic checking of the virtual terminals.

The invention relates to the technical field of natural language processing, and discloses a word segmentation method and device, equipment and a storage medium. The method comprises the following steps: for any sentence in a text, segmenting the sentence to obtain a plurality of word segmentation results corresponding to the sentence; for any word segmentation result in the plurality of word segmentation results, according to a word vector of any word segmentation in the word segmentation result, based on a frequency domain vector converted by the word vector and a font vector of the word segmentation, determining fusion features corresponding to the word segmentation; wherein the frequency domain vector of the segmented word is obtained by processing the word vector of the segmented word based on Fourier transform, and the font vector of the segmented word is obtained according to the stroke of the segmented word; and determining a target word segmentation result corresponding to the sentence from the plurality of word segmentation results according to the fusion feature corresponding to each word segmentation in each word segmentation result in the plurality of word segmentation results. Therefore, the accuracy of the word segmentation result can be improved.

The application provides a Chinese relation extraction method and system based on multi-modal semantic fusion, and relates to the technical field of information extraction. The method comprises: obtaining a Chinese sentence and entities corresponding to the Chinese sentence; extracting text semantics, shape semantics and structure semantics of each Chinese character in the Chinese sentence; constructing a multi-modal semantic fusion model through an improved Transformer network, encoding the shape semantics and the structure semantics respectively, splicing the encoded semantic features to obtain auxiliary features, taking the text semantics as main features, optimizing the feature distribution of the main features according to the correlation coefficient between the main features and the auxiliary features, and then obtaining the fused multi-modal semantic features; and determining the Chinese relation between the entities according to the multi-modal semantic features. In this way, the shape semantics and the structure semantics of the Chinese characters are used to enrich the context information of the Chinese sentence, which can reduce the influence of Chinese ambiguity in Chinese relation extraction and improve the Chinese relation extraction effect.

To provide a novel technique for more natural dialogue in Japanese natural language processing.SOLUTION: A computer stores in a storage unit element data on elements that constitute a sentence, a template that defines a combination of a plurality of elements, and decontaminated information acquisition data that specifies elements on the template, selects, on the basis of a text constituting an input sentence and a combination of the elements on the template, a template corresponding to the input sentence, and stores, on the basis of on the selected template and the decontaminated information acquisition data, decontaminated information based on the elements on the template.SELECTED DRAWING: Figure 14

Disclosed herein is a system including an answer determination module configured to analyze an input sentence to determine whether to answer the input sentence, a learning module configured to output a domain corresponding to the input sentence and a plurality of categories to which the input sentence belongs when it is determined to answer the input sentence, and an output module configured to output an answer to the input sentence, wherein the learning module performs multi-task learning using the input sentence as input data and using as output data the domain corresponding to the input sentence and the plurality of categories to which the input sentence belongs.

The application relates to the technical field of artificial intelligence, and discloses a word weight sorting method, device and equipment and a storage medium, the sorting method comprising the following steps: obtaining a first target sentence, performing word segmentation on the first target sentence to obtain a first vocabulary set contained in the first target sentence; removing each first vocabulary in the first vocabulary set from the first target sentence to obtain a second target sentence; forming a sentence pair by combining any second target sentence and the first target sentence, inputting the sentence pair into a pre-trained language model to generate a vector representation corresponding to the sentence pair; determining a target similarity between the second target sentence and the first target sentence in the sentence pair according to the vector representation corresponding to the sentence pair; sorting the target similarities of the second target sentences and the first target sentence; and determining a weight order of each first vocabulary according to the sorting of the target similarities. The application solves the problem that the word weight cannot be accurately sorted in the prior art.

The application discloses a sentence pair semantic matching method and system, belongs to the natural language processing technical field and the computer artificial intelligence field, and aims to solve the technical problems of how to construct and utilize context information of a sentence pair to strengthen a sentence pair interaction process and how to realize direct interaction between the context information and the sentence pair, thereby improving the accuracy of sentence pair semantic matching. The method specifically comprises the following steps: obtaining a sentence pair semantic matching data set; downloading a publicly disclosed sentence pair semantic matching data set from a network; constructing a sentence pair semantic matching model based on a Bilinear Triple-Attention mechanism; and training the sentence pair semantic matching model on a sentence pair semantic matching training data set. The system comprises a data set obtaining unit, a model constructing unit and a model training unit.

The application discloses a text false information detection method and system fusing contradictory features, and the method comprises the following steps: performing data preprocessing on a given input text, extracting text features, and extracting sentence pairs with a similarity higher than a threshold to form a similar sentence pair dataset; extracting contradictory word vector features, contradictory scene features and contradictory semantic features in the given input text based on the similar sentence pair dataset; fusing the text features, the contradictory word vector features, the contradictory scene features and the contradictory semantic features, weighting through a self-attention mechanism, and obtaining a weighted and distributed feature fusion vector; and performing false information detection based on the feature fusion vector, and obtaining a false information detection result. The application can fuse contradictory features and style statistical features of the text to perform false information detection, and can effectively improve the accuracy of text-based false information detection.

The invention discloses a document-level machine translation method based on multi-granularity knowledge enhancement and a related device, and relates to the technical field of document-level machine translation.The method comprises the steps that a to-be-translated source document using a source language is segmented to obtain a plurality of sub-documents, multi-granularity knowledge is generated through a large language model, and the multi-granularity knowledge is obtained; comprising global knowledge including a global source language abstract, a global target language abstract, a global proper noun set and global topic description, and local knowledge including a core topic and a transition prompt of each sub-document, and translating each sub-document by using a large language model under multi-granularity knowledge enhancement, and performing sentence alignment on the sub-documents and the translated sub-documents, and if the sentence alignment succeeds, removing overlapped sentences in each translated sub-document by using a large language model and then performing splicing to obtain a translated target document using a target language. The translation quality can be improved.

The application relates to the technical field of artificial intelligence, in particular to an emotion classification method and device based on artificial intelligence, computer equipment and a medium. The method encodes each sentence in a dialogue text into a sentence embedding vector and inputs the sentence embedding vector into a classifier to obtain an emotion probability distribution of the corresponding sentence, divides the dialogue text into sentence sets according to speakers, calculates the transition probability between the emotion probability distributions of any two adjacent sentences in the sentence sets to obtain a speaker transition probability vector, constructs a target function according to the speaker transition probability vectors of all sentence sets and the emotion probability distributions of all sentences, optimizes and solves the target function, determines the result corresponding to each sentence from the solution result as an emotion classification result, respectively models the transition probability of the sentence sets of different speakers, effectively extracts the emotion change information of different speakers themselves, jointly solves the emotion classification result according to the emotion change information and semantic information, and improves the accuracy of emotion classification.

The embodiment of the invention provides an evaluation method and device for a retrieval enhancement generation model and computing equipment, and the method comprises the steps that a preset text is obtained, the preset text comprises at least one question and answer statement pair, and the question and answer statement pair comprises question and answer statements and reference answers corresponding to the question and answer statements; the preset text is processed through the retrieval enhancement generation model, a standard evaluation index and at least one special evaluation index of the retrieval enhancement generation model are obtained, and the special evaluation index is used for representing the performance of the retrieval enhancement generation model in the target dimension; and determining an evaluation result according to the standard evaluation index and the special evaluation index. According to the method, the applicability of the model evaluation method can be improved, and an accurate improvement direction is provided for model optimization in different application scenes.

The invention discloses an open source software license clause detection method based on a graph neural network, and the method comprises the following steps: S1, carrying out the text preprocessing of a given to-be-analyzed license document, and obtaining a simplified sentence lexical element list; s2, initializing and constructing a structure chart corresponding to each sentence, and obtaining a structure chart list; s3, initializing and constructing a content graph corresponding to each sentence to obtain a content graph list; s4, by adding a common root node, respectively connecting the structure diagram and the content diagram corresponding to each sentence, thereby creating a combination diagram corresponding to the sentence, and finally obtaining a combination diagram list; and S5, traversing the combination graph list, constructing a graph neural network, calculating an attention coefficient between any two nodes in each combination graph, and finally obtaining and outputting a result list. According to the method, the sentences related to the specific terms in the given open source software license document can be automatically, efficiently and accurately recognized and extracted.

The application provides a large model low-resource language translation method and system based on reasoning and retrieval fusion, which comprises the following steps: obtaining enhancement information, which contains retrieved similar parallel sentence pairs and extracted auxiliary knowledge; performing first-stage implicit correction, constructing a comparative demonstration context containing retrieved example source sentences, auxiliary knowledge, initial translation and standard reference translation, inputting the large model with the to-be-translated source sentence and initial translation, and generating first-stage corrected translation; performing second-stage explicit correction, performing error detection and labeling on the first-stage corrected translation based on the large model according to multi-dimensional quality measurement standards, generating structured feedback, and inputting the large model again to generate second-stage corrected translation. The application effectively improves the accuracy and robustness of low-resource language translation.

Systems and methods are provided for generating language learning items. In embodiments, training data comprising a plurality of concept-sentence pairs received by a machine-learning based language model may be used to train the model to generate sentences based on an input. A stimulus may be received and used by the trained machine-learning based language model to generate a sentence. The sentence may be stored in a computer readable medium.

A method and apparatus for generating a captioning device, and a method and apparatus for outputting a caption. The method for generating a captioning device comprises: acquiring a sample image set; inputting the sample image set into an image encoder of a sentence generator, so as to output an object set; grouping the object set into a first object set and a second object set, wherein the first object set is an object set that is included within a preset object set, and the second object set is an object set that is excluded from the preset object set; inputting, into a sentence decoder of the sentence generator, the object set output by the image encoder, and performing a beam search in a decoding step by taking the first object set and the second object set as constraint conditions, so as to generate a pseudo-image sentence pair set; and training the sentence generator by taking the pseudo-image sentence pair set as a sample set, so as to obtain a captioning device.

The embodiment of the invention provides a semantic vector generation method and device, equipment and a storage medium, and relates to the technical field of vector matching. The method comprises the steps of obtaining a CQL statement pair corresponding to a fine-tuning query statement from a preset knowledge graph, obtaining a recalled text according to a node tag related to the CQL statement pair, generating a fine-tuning sample after obtaining a positive sample and a negative sample based on the recalled text, training a vector model by using the fine-tuning sample to obtain a fine-tuning loss value, and determining the fine-tuning loss value according to the fine-tuning loss value. And performing parameter adjustment on the vector model based on the fine adjustment loss value until the training is finished to obtain a trained target vector model. Vectorization training is performed on the fine adjustment query statement and the positive / negative sample through the vector model, so that the vector model can learn a semantic association rule in the knowledge graph, and different knowledge graph search scenes can be quickly adapted. Therefore, the text matching result based on the knowledge graph keeps accurate semantic information and also has good scene adaptability, and the accuracy of text matching in a search scene is remarkably improved.