39 results about "Adnoun" patented technology

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

The present invention provides a method, device, and robot for generating knowledge for solving applied problems, comprising: obtaining input text corresponding to a target applied problem input by a user; segmenting the input text into sentences and assigning a corresponding sentence number to each acquired text sentence; obtaining the noun content associated with each text sentence to form noun content recognition knowledge; and matching noun content extension knowledge associated with the input text from a pre-constructed knowledge base based on the noun content associated with each text sentence and its corresponding sentence number. The method, device, and robot for generating knowledge for solving applied problems provided by the present invention can generate noun content extension knowledge that is not contained in the applied problem itself but is of great value in solving the problem, thereby more effectively assisting the development of various systems related to applied problems and improving the accuracy of problem solving in various systems to a certain extent.

The invention discloses a method and system for integrating retrieval and enhancing generation ability, and the method comprises the steps: S1, building a basic SQL set, and enabling each basic SQL to correspond to a preset business intention and to comprise a placeholder condition field; s2, establishing a mapping relation between fields and noun types; s3, performing intention analysis by using a large language model, and matching a basic SQL; s4, performing query condition reasoning, extracting specific nouns, and executing expansion word generation and compound word processing; s5, generating a complete SQL according to rule driving; s6, executing the SQL and dynamically correcting a query result to enable the query result to meet a context length requirement; and S7, inputting the question and result combination into the large language model to generate an answer. The method does not need a vector database, adapts to a small parameter model, is low in cost and high in real-time performance, and can be widely applied to various traditional service systems needing to integrate the AI dialogue ability.

This invention relates to the field of information retrieval structure technology, specifically to a method and system for monitoring data during the investment promotion process in industrial parks. The method includes the following steps: extracting investment promotion text, identifying combinations of verbs and resource nouns, analyzing word order and semantic features, filtering action-driven segments, dividing structural sequences and identifying connection relationships, establishing semantic jump paths, reorganizing sentence order, and generating a data monitoring structure template for investment promotion in industrial parks. In this invention, by extracting verbs and resource nouns to construct sentence content, combining word order features and semantic tags to classify and filter segments, dividing structural sequences based on the position of resource nouns and merging content with consistent semantic direction, mapping connectors to jump paths to construct task behavior sequences, and realizing the logical organization and sequential reorganization of sentences, this generates a data expression method with traceability and structured features, improving the semantic recognition efficiency and data monitoring capabilities during the investment promotion process in industrial parks.

The invention aims to provide a method and equipment for correcting regional noun errors. Compared with the prior art, the method and the device have the advantages that the preset number of correct corpora containing the regional nouns are constructed, the regional nouns in each corpus are subjected to erroneous character construction based on the preset erroneous character construction rule, the constructed erroneous character corpus is determined, model training is performed based on the correct corpora and the erroneous character corpus, and the trained error correction model is determined; wherein the error correction model comprises region noun recognition and region noun error correction, and then region noun error correction is carried out on sentences containing region noun based on the trained error correction model. In this way, the effect of correcting regional nouns can be improved, and the error correction accuracy can be improved by assisting the text error correction function through the entity recognition function.

To accurately quantify the content characteristics of a document.SOLUTION: An information processing device 1 is connected to a service server 3, such as an SNS, via a communication line. The information processing device is equipped with means for collecting data regarding posts made by a specific user on an SNS service or the like from the server, means for performing natural language analysis processing on the posts and breaking them down into parts of speech, means for extracting multiple character strings corresponding to nouns among the parts of speech, and means for obtaining, as feature quantities that quantify the content characteristics of the posts, coordinates in an N-dimensional space where each of N categories classifies the extracted multiple character strings according to the meaning of the nouns, each axis corresponds to one category, and each axis is assigned with many character strings representing concrete subordinate concepts included in that category, arranged in order according to their semantic similarity, with each coordinate value pre-assigned.SELECTED DRAWING: Figure 1

This invention discloses a slow-fast diversity-aware prototype learning method for egocentric action recognition. This method utilizes a part-to-prototype (P2P) module and a slow-fast contextualization (SFC) mechanism. In P2P, partial tokens obtained from each frame by the video converter are further interacted with several initial prototypes through diversity-aware attention, refining / extracting the semantic information of the interacting objects from the partial level to the prototype level. In SFC, slow and fast awareness attention is introduced to interact with the original prototypes to finely supplement semantic information. Three streaming features are aggregated using slow and fast gates, ultimately yielding classification results for nouns and verbs.

This invention relates to a prompt-based machine translation method, belonging to the field of natural language processing technology. It solves the problems of inaccurate, omitted, and mistranslated nouns and proper nouns in existing machine translation models. By constructing a set of nouns and their translations from the text to be translated, the input text and adjustment matrix of the translation model are obtained. The translation model is then used to translate the input text, and the attention calculation of the model is adjusted using the adjustment matrix M, ultimately outputting the translation. Based on the input data containing noun translation prompts and the adjustment matrix, the accuracy of the noun translation by the translation model is guaranteed to a certain extent, solving the problems of omitted and mistranslated nouns, and improving the accuracy of noun translation in machine translation models.

The invention provides a character correction method and device and electronic equipment, and the method comprises the steps: obtaining a to-be-processed character, and enabling the to-be-processed character to be obtained through the conversion of voice data collected in real time; extracting to-be-processed nouns from to-be-processed characters by using the trained name recognition model; searching a target name of which the semantic similarity with the to-be-processed noun is greater than a semantic similarity threshold value; judging whether the phonetic similarity between the target name and the to-be-processed noun is greater than a phonetic similarity threshold value or not; and if yes, correcting the to-be-processed noun in the to-be-processed character as the target name. In the implementation process of the scheme, a dual verification mechanism combining semantic similarity and phonetic similarity is adopted, the semantic similarity ensures that candidate words conform to context logic, the phonetic similarity ensures that pronunciation or fonts of the candidate words are close to those of original words, the problems of excessive error correction and correction omission possibly caused by a single standard are avoided through combination of the semantic similarity and the phonetic similarity, and the accuracy of correction is improved. Therefore, the accuracy of converting voice into characters is obviously improved.

The invention relates to the technical field of intelligent document review, in particular to an intelligent document review method and system based on large model fusion causal reasoning, and the method comprises the steps: collecting documents, extracting formats and null lines, recognizing roles and actions, retrieving nouns after verbs, processing semantic repetition, outputting a table, checking causal connections, comparing action sequences, screening and joining, ranking verbs, and pulling through actions. And outputting causal fragments, checking consistency, separating objects, calibrating behaviors, outputting chain fracture, splitting adverbs, sorting relationships, and outputting conflict paths and review paths. According to the method and the device, by extracting the paragraph formats, roles and actions of the documents, the meticulousness of review is improved. And analyzing a causal conjunction and an action relationship, and identifying a causal relationship and a semantic conflict. The method comprises the steps of calibrating an action sequence, identifying missing parts, sorting multiple actions and role changes, optimizing an examination path, reducing manual intervention and improving efficiency.

The application belongs to the field of natural language processing, and particularly relates to a text key phrase extraction method, a storage medium and a device that integrate inter-sentence correlation, comprising: extracting a nominal phrase in a part-of-speech combination mode; filtering the nominal phrase by combining two Trie trees to obtain a candidate phrase set; calculating a global semantic similarity score of the candidate phrase; clustering each sentence to obtain a sentence cluster containing different semantic information; and sorting the candidate phrases in the sentence cluster according to the global semantic similarity score of the candidate phrase to obtain a key phrase set; the application integrates the inter-sentence relationship in the unsupervised key phrase extraction model based on embedding, improves the accuracy of the model, and constructs different Trie trees to calculate mutual information and left and right information entropy as a candidate phrase extraction method in the key phrase extraction model, thereby reducing the probability of incomplete semantic information appearing in the extracted candidate phrase set.

The invention discloses an object illusion relieving method and device for a multi-modal large language model, and the method comprises the steps: inputting a picture and a description instruction into a large visual language model for analysis, so as to generate a candidate description set corresponding to the picture; extracting an object noun of each candidate description in the candidate description set to obtain an object set; obtaining a conditional instruction, and calculating a conditional probability corresponding to each object noun in the object set according to the conditional instruction to obtain a confidence coefficient corresponding to each object noun; obtaining a final description corresponding to the picture according to the confidence corresponding to each object noun; therefore, by reducing excessive dependence of a large visual language model on language priori, the object illusion problem in an image description task is effectively relieved.

Embodiments of the present disclosure relate to a method, an apparatus, an electronic device, and a medium for text translation. The method includes determining a keyword set associated with a chapter-level monolingual corpus in a target language, where the keyword set includes a plurality of entity words and a plurality of pronouns, and masking the chapter-level monolingual corpus based on the keyword set. The method further includes generating a chapter-level text translation model based on the masked chapter-level monolingual corpus. According to the embodiments of the present disclosure, it is possible to enable translations of the same or associated words to have contextual consistency throughout a text, and to explicit a noun indicated by a pronoun, and further to supplement a missing pronoun, thereby improving accuracy of the text translation model.

The application relates to the technical field of natural language processing, and discloses a Chinese numeral quantifier correction method and system, computer equipment and a storage medium. The application constructs a dynamically expandable collocation word table and a numeral dictionary; performs sentence segmentation, word segmentation and part-of-speech tagging on input text, and identifies a three-element structure composed of numerals, quantifiers and nouns; excludes misjudgments on inherent expressions through a fixed phrase filtering mechanism; performs legality verification on the structure based on the collocation word table, and generates processing information for semantic correction if the verification fails; when collocation abnormalities are confirmed and the dictionary is insufficient, a large language model is triggered to replace the quantifier and adapt to the context based on the processing information, so that a final corrected sentence is generated. Through multi-layer cooperation of rule matching, word table verification and large model verification, the application effectively reduces the mis-correction rate while ensuring high-precision correction, and improves the accuracy and practicality of automatic correction of Chinese quantifiers.

The present disclosure provides a new word screening method and device and electronic equipment, relates to the technical field of data processing, and is used for solving the problem of how to quickly and effectively identify new words in the field of text processing and information mining. The method comprises the following steps: obtaining a text to be analyzed; performing text processing on the text to be analyzed to determine at least one text segment; determining a statistical parameter of each text segment according to the total number of text segments and the total number of each text segment; in the case that the statistical parameter meets a preset condition and the characters contained in the text segment meeting the preset condition are weak words, determining that the characters contained in the text segment meeting the preset condition are new words; wherein the weak words include words that have been used in the target field but are not nouns.

The application relates to an artificial intelligence technology and discloses a question expansion method and device, equipment and a storage medium. The method comprises the following steps: extracting an inquiry mode word and an entity noun in a to-be-expanded question; extracting a standard question containing the inquiry mode word and a synonymous question with the same meaning as the standard question in a question and answer library, and marking the inquiry mode word and the inquiry mode word contained in the synonymous question as a keyword; performing word segmentation and part-of-speech tagging on the standard question and the synonymous question to obtain a sentence structure; extracting a front adjacent substantive word and a rear adjacent substantive word of the keyword in the standard question and the synonymous question according to the sentence structure; extracting the inquiry mode word in the keyword, and extracting the front adjacent substantive word and the rear adjacent substantive word; and according to the keyword, the front adjacent substantive word, the rear adjacent substantive word and the proper noun, composing a preset number of expansion questions in a preset grammar format. The application can automatically generate expansion questions according to an input question.

The embodiment of the present disclosure provides a kind of aerospace field-oriented open vocabulary target detection method and device, it is related to target detection technical field.The method comprises: the image to be detected and the description text corresponding to the image to be detected are input into the detection model pre-trained, obtain at least one mark frame and the target prediction category of the object in each mark frame of detection model output, target prediction category is the entity noun included in description text.Detection model is obtained according to pre-constructed sample dataset training, sample dataset includes self-built dataset;For each sample image in self-built dataset, the real category of sample object is used as training label, real category includes entity noun and extension word, by jointly as training label with key word and extension word, it can directly incorporate domain expertise into model training, enhance the semantic understanding ability of detection model in aerospace field, effectively improve the generalization ability and accuracy of detection model identification unseen class.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

We provide the system. [Solution] A natural language processing means that analyzes text data and divides it into nouns and punctuation units, A speech synthesis means that generates speech data in multiple languages ​​based on analyzed text data, Image generation preparation means for preparing data for image generation based on analyzed text data, An image generation means that generates images corresponding to each scene in real time based on data for image generation, A synchronized playback means that outputs audio data and generated images in sync, A system that includes this.

The present disclosure provides a text correction model training method, a text correction method and a device, and relates to the technical field of computers, in particular to the field of natural language processing. The text correction model training method comprises: obtaining a sample sentence and an actual reference object of a pronoun in the sample sentence, and obtaining a historical sentence of the sample sentence; performing a first specified length mask on the pronoun in the sample sentence; the first specified length is determined based on the character length of the candidate noun in the historical sentence; and training the text correction model based on the sample sentence, the historical sentence, the masked sample sentence and the actual reference object of the pronoun until the text correction model converges. The technical solution of the present disclosure can obtain a text correction model with high precision, thereby improving the accuracy of text correction and improving the user experience.

Embodiments of the present application provide a kind of allegory sentence pattern component extraction method, device, computer readable medium and equipment.The method comprises: determining the part-of-speech result, part-of-speech mask matrix and adjacent matrix based on syntax dependency relationship corresponding to the text to be processed;Word meaning retrieval is carried out to the part-of-speech of noun, and the noun interpretation set corresponding to the text to be processed is obtained;The text to be processed and the noun interpretation set are spliced, and input into BERT encoder, and the corresponding text representation matrix is obtained;The text representation matrix is multiplied by the part-of-speech mask matrix, and the corresponding word node matrix is obtained;Based on GAT algorithm, the word node matrix and the adjacent matrix are updated to obtain the node representation corresponding to the text to be processed;Based on the node representation, the allegory component extraction of the text to be processed is carried out.The technical scheme of the embodiment of the present application improves the accuracy of component identification and extraction in allegory sentence pattern.

The application relates to a method for associating entities in text to a knowledge base by using context, and belongs to the technical field of natural language processing. The method comprises the following steps: establishing a knowledge base; establishing a knowledge base index; preprocessing characters of entity references in input text, obtaining a candidate entity set corresponding to the entity references; establishing a pair of <reference, entity>; selecting multiple candidate entity description sentences with high context relevance to the entity references as selection basis; and judging entity nouns in the knowledge base corresponding to the entity references or judging that there is no entity noun in the knowledge base corresponding to the entity references according to the selection basis. The method provided by the application can expand the matching range and effectively improve the accuracy of association; the MLP multi-layer perception model which cannot link the classification tags is added at the prediction result mark to judge the entity nouns corresponding to the entity references, so that the problem that, in the prior art, when the entity is not in the knowledge base, only the item with the maximum association degree is found to cause an error association is solved.

The invention relates to a method for analyzing a language structure by using a language expression, which is suitable for analyzing structures of more than 7,000 languages on the earth. The method comprises the following steps of: analyzing a morphologic element and a syntactic structure of a target sentence by a morphologic element and syntactic analysis unit; and based on a pre-training language expression learning algorithm provided by a language expression learning algorithm unit, a language expression calculation unit generates a language expression for the morphologies and syntactic structures of the sentences. Wherein the language expression calculation unit is used for dividing a sentence into an upper left area, a lower left area, an upper right area and a lower right area by taking a sentence center symbol as a benchmark, a subject symbol is distributed in the upper left area, a core verb symbol is distributed in the lower left area, and a verb type symbol is distributed in the lower right area; non-predicate symbols representing remaining phrases other than subjects and verbs are assigned to the right upper region. Furthermore, symbolization specific information including marks (1-4) indicating a person and a character number mark (.) indicating the number of word characters such as nouns and adjectives is added to at least one of the left upper mark, the left lower mark, the right upper mark and the right lower mark of each symbol. By means of the method, the sentences in the specific language can be accurately analyzed into structured syntactic expressions, and therefore the efficiency of language processing such as sentence recognition, semantic understanding, translation and retrieval is improved.

The present invention provides a fine-grained information extraction method and system based on part-of-speech tagging, the method comprising: encoding and information extraction strategies of pre-stored typical example sentences; performing phrase-level word segmentation on the sentence to be analyzed and tagging the phrases with parts of speech; merging and hiding adjacent phrases according to the encoding strategy; replacing nouns, verbs, and verb phrases in the sentence with parts of speech based on the parts of speech and sentence structure marked by S to form an encoding of the sentence to be analyzed; matching the encoding of the sentence to be analyzed with the encoding of pre-stored typical example sentences; if there is a match with the pre-stored typical example sentences, extracting information from the sentence to be analyzed according to the extraction strategy of the matched typical example sentences; if there is no match, extracting information from the sentence to be analyzed according to the information extraction strategy; and storing the encoding of the sentence to be analyzed and the information extraction strategy. The information extraction process of the present invention can quickly adapt to new information extraction needs.

The method for mapping a noun phrase to a description logic concept based on epitaxy firstly exhaustively lists all text segments of the noun phrase, generates a mapping table of the text segments to resources in a knowledge base; then generates an analysis sequence according to the word segmentation, part-of-speech tagging and syntax tree of the noun phrase; and finally, according to the analysis sequence, continuously refines basic concepts generated by the indexed resources from the concept of EL++, until all words are analyzed, to obtain the description logic concept to which the noun phrase is mapped. The application can automatically process complex noun phrases containing implicit relations and generate high-quality description logic concepts through analysis of the syntax tree.

The application provides a Chinese event detection method based on a part-of-speech attention mechanism, which is based on a public data set. First, the sentence is divided into words using a word segmentation tool on the data set, and then the part-of-speech sequence of the sentence is obtained using a part-of-speech tagging tool. The part-of-speech sequence of the sentence is input into a CBOW model to obtain a pre-trained part-of-speech vector, so as to learn the fixed collocation information between words, such as "suffered an injury" as a "verb + adverb + noun" structure. Then, the part-of-speech vector, the word vector and the character vector are used to extract the word-level information and the character-level information of the sentence, respectively. When extracting features, a convolutional neural network is used to extract features on the word matrix, the character matrix and the part-of-speech matrix of the sentence. Then, the part-of-speech features are used to calculate the attention score, which is used to assist the model to focus on the verb when calculating; the part-of-speech attention is provided, and after the part-of-speech features are added, the accuracy and efficiency of the model in the trigger word extraction and event type classification tasks are higher.

This application provides a method, apparatus, electronic device, and storage medium for constructing a multi-domain knowledge graph, relating to the field of knowledge graph construction technology. The method includes: constructing a text dataset and performing domain filtering based on a pre-selected large language model to obtain domain text datasets for target domains (single-domain, related-domain, and full-domain); further performing sentence segmentation, word segmentation, and part-of-speech tagging; extracting nouns from the segmented word dataset to obtain target entities; performing word segmentation and dependency syntax relation extraction on each sentence in the target domain text dataset, and reconstructing a dictionary list of target dependency syntax relations to extract relationships between different domains; and constructing a multi-domain knowledge graph based on entities and relations. The multi-domain knowledge graph constructed in this application is more comprehensive, accurate, and efficient, requiring no large amounts of data for model training, thus saving significant time, manpower, and computational resources.

Techniques are disclosed for improved autonomous agents that can provide a recommendation in a non-intrusive, conversational manner. In an aspect, a method determines a first sentiment score for a first utterance and a second sentiment score for a second utterance, each sentiment score indicating an emotion indicated by the respective utterance. The method further identifies that a difference between the first sentiment score and the second sentiment score is greater than a threshold. The method further extracts a noun phrase from the second utterance. The method identifies a text fragment that includes an entity that corresponds to the noun phrase. The method identifies that the text fragment addresses a claim of the second utterance. The method forms a third utterance that includes the a recommendation related to the second utterance and adds the third utterance to the sequence of utterances after the second utterance.