335 results about "Named entity" patented technology

The invention discloses a geological domain named entity accurate recognition and classification method based on thinking chain enhancement and hybrid expert architecture, which is characterized by comprising the following steps: firstly, extracting geological document text data through an OCR (Optical Character Recognition) technology, and extracting structured entity data by utilizing a locally deployed large language model; then calling a local large model to generate a diversified sentence pattern template according to language styles in the geological field, filling the template with the extracted professional entities, and constructing an instruction fine tuning data set; further constructing a thinking chain (CoT) enhanced data set on the basis, and explicitly simulating an expert reasoning process; efficient fine tuning is carried out on the large model by innovatively combining a low-rank adaptation (DoRA) technology and a hybrid expert (MoE) architecture, the DoRA technology carries out dimension reduction decomposition and orthogonal transformation on weight matrixes of a decoder layer and a multi-layer perceptron, and the MoE architecture constructs a plurality of special sub-networks to enhance the multi-task processing capability; and finally, performing entity extraction on the geological document by using the fine-tuned model, outputting an identification result containing a reasoning process, and filtering and perfecting the result through a rule matching mechanism. According to the method, the problems of fuzzy boundary, indefinite semantics, difficulty in classification and the like of the named entities in the geological field are effectively solved, the recognition and classification accuracy of the named entities in the geological field is remarkably improved, and key technical support is provided for downstream applications such as geological resource exploration and mineral evaluation.

The invention belongs to the technical field of power system informatization, and provides a multi-modal power knowledge base construction method and retrieval method based on an AI large language model.The method comprises the following steps that S1, classification and named entity extraction are conducted on an original database, and a data set and an entity set are obtained; s2, aligning the data set and the entity set based on an AI large language model; s3, extracting and complementing entity relationships, and constructing an electric power diagram database; s4, performing joint embedding on the entities and the associated data, mapping the entities and the associated data to the same vector space, constructing a multi-modal power knowledge base, and generating a data index; and performing retrieval by using the constructed multi-modal power knowledge base, and performing source file backtracking through a data index. According to the method, multi-modal data are fused, the constructed multi-modal electric power knowledge base is rich and accurate, and source file backtracking can be carried out.

A system for matchmaking using a conversational agnostic matchmaking model is described. The system can receive a first query indicating a request for document objects and including criteria for selection of the document objects. The system can identify named entities from portions of the first query. The system can generate a second query to obtain the document objects, in response to the named entities being indicative of a context for the first query. The system can obtain the document objects according to the second query. The system can generate a reply to the first query including a description object and the documents, the description object based on the first query. The system can cause a user interface to present the reply to the first query and the description object.

The invention discloses an entity standardization method based on big language model retrieval enhancement, and provides a biomedical entity standardization model generated based on big language model retrieval enhancement. The model converts a named entity standardization task into a question and answer task by constructing a prompt template, and the prompt template is a query about entity mention and comprises the entity mention, context and a candidate entity list. According to the method, the interaction between the entity mention and the standard entity, the interaction between the entity mention and the context information and the interaction between the candidate entities can be effectively simulated. In addition, in order to ensure the consistency of the entity mention and the context information of the candidate entity, the entity description information of the candidate entity is added into the prompt template.

Named Entity Recognition is the identification and classification of named entities within a document. Nested NEs occur when an NE is contained within another NE. The disclosed invention leverages the CapsNet architecture for improved nested NE identification and classification. This includes deriving the features of an input text. The derived features are used to identify and classify any named entities in the text. The system is further configured to identify named entities in the text and perform clustering to group named entities. The disclosed CapsNet considers the context of the whole text to activate higher capsule layers in order to identify the named entities and classify them. The teachings of this invention are applicable to other NER models to improve nested NE identification and classification.

The invention discloses a geological text translation method based on a large language model and retrieval enhancement generation, and aims to identify a geological text named entity as a keyword and retrieve and query a professional dictionary database to perform enhancement translation. According to the method, when entity recognition is carried out on a fine tuning large language model, a syntax-aware entity pruning (SAEP) method is provided for data enhancement, controllable noise is introduced, and the named entity recognition effect of the large language model is improved. When a vector database is constructed and retrieved, geological classification labels are added to data information based on a data level, and a data similarity query threshold value is set, so that the accuracy of information retrieval is improved, and the illusion problem of a general large language model caused by the lack of professional domain knowledge of training data is effectively reduced.

Improved systems and methods for named entity recognition (NER) are disclosed and can include attaching domain-specific context to extracted data. In a particular example implementation, the techniques can include an artificial intelligence (AI) based entity extraction and labeling process using unstructured data as input. The generated labels can include automatically determined entity types. The techniques can further include a domain-aware entity resolution process. First, applying a reverse question-and-answer (Q&A) technique to the output of the entity extraction and labeling process can generate a set of predicted entity keys (e.g., predicted metadata identifiers, such as likely database column references) for the extracted entities and entity types. Second, entity alignment operations can enable determining domain-specific entity keys for the predicted entity keys. In some implementations, the techniques can be utilized to identify named entities in an electronic conversation, such as a chat session.

The invention discloses a relation enhancement type named entity recognition method and device, a medium and a program product, and relates to the field of data processing. The method comprises the following steps: performing feature extraction on text data through a BERT model to obtain a plurality of tokens and semantic feature vector sequences; performing different-scale convolution operations by using a convolutional neural network to obtain local character combination features, and fusing the local character combination features with the semantic feature vector sequence to form a feature enhanced semantic feature vector sequence; the Transform model determines a long-distance dependency relationship of semantic feature vectors in the sequence by using an attention mechanism; generating a long entity structure model based on the long-distance dependency relationship, and then determining an enhanced semantic feature vector sequence; and performing tag sequence optimization processing on the named entity through a conditional random field layer, determining sequence labeling loss, boundary detection loss and relation prediction loss, determining total loss, and adjusting a tag sequence so as to determine the named entity. According to the method, the accuracy and integrity of named entity recognition can be effectively improved.

A system and method for classification. A method includes identifying candidate entities among text data by applying at least one entity identification rule to the text data. Inputs are constructed based on the identified candidate entities, where each input includes a first portion of text indicating a candidate entity and at least one second portion of text and where the at least one second portion of text of each input is adjacent to the first portion of text of the input. Multiple language models are applied to the inputs, where each language model is trained to identify a respective set of entities and where outputs of the language models include at least one portion of entity-indicating text for each input. Based on the outputs of the language models, at least one named entity in the text data is determined.

The present invention discloses a supply chain material data management method and system, the method comprising receiving material data of upstream and downstream enterprises in the supply chain; performing named entity recognition for each material data, and analyzing the semantic dependency of attributes using dependency analysis technology; mapping different entities to corresponding concept nodes in an ontology, and establishing associations between entities and concepts; using a constructed material ontology, performing semantic standardized representation of entities, mapping entities to corresponding concept nodes in the ontology, and replacing them with standardized concept names or identifiers to form a standard material attribute feature library. The present invention realizes the collection and standardized processing of material data of upstream and downstream enterprises in the supply chain and the precise association of upstream and downstream material data.

Disclosed are methods and system for predicting data-source influences on one or more data manifestations of a named entity. The method includes receiving an inheritance dataset of the named entity. The method determines first and second portions of the inheritance dataset of the named entity. The method determines an aggregated data-bit association score for the named entity based on the inheritance dataset at an identified subset of the data-bit regions. The method determines aggregated data-bit association scores associated with the first and second data source based on the first and second portions of the inheritance dataset at the identified subset of the data-bit regions. The method selects one of the first and second data sources as having a measure of influence on a data manifestation of the named entity corresponding to the identified subset of the data-bit regions.

The invention relates to a maintenance conflict processing method and system based on a large language model. The method comprises the following steps that power grid maintenance documents are collected to construct a word list, and a prompt word template is set; converting the segmented document into a high-dimensional semantic vector to construct a vector database; matching contents in a database, adding a cue word template, generating a named entity extraction result by using a plurality of large language models, replacing a document entity with an entity type to construct a predicate proximity graph, and aligning predicates by comparing a relation semantic type; generating entity vector representation by using a TransE method, aligning different map entities and judging a corresponding relation; based on the cue word template and the domain word list, the power grid maintenance document entity is identified, the conflict information is extracted, the conflict information is integrated in combination with a multi-model mechanism, conflict identification and mediation between maintenance tasks are achieved, and the power grid maintenance efficiency and safety are improved.

The invention discloses an intelligent equipment fault relation extraction method based on multi-semantic knowledge interaction and dynamic pruning. Performing word segmentation and sentence segmentation on the original equipment fault text to obtain a statement set; obtaining a character-level word embedding representation set of the text by using a pre-training language model; extracting fault entity representations, obtaining fault type representations based on the constructed fault type knowledge base, constructing semantic association between the fault type representations, and generating a context feature representation sequence; calculating a correlation score between the enhanced semantic representation and an original text statement based on a dot product attention mechanism, dynamically selecting a core statement set, focusing the core statement, updating the semantic representation, and inputting the semantic representation into a relationship classification module to predict a relationship type between the entity pairs; and finally, outputting a named entity boundary and a relation label based on conditional random field decoding to obtain a final relation extraction result. According to the method, the ability of understanding the context is enhanced, and the fault relation extraction task under the complex context can be better processed.

Techniques for preprocessing data assets to be used in a natural language to logical form model based on scalable search and content-based schema linking. In one particular aspect, a method includes accessing an utterance, classifying named entities within the utterance into predefined classes, searching value lists within the database schema using tokens from the utterance to identify and output value matches including: (i) any value within the value lists that matches a token from the utterance and (ii) any attribute associated with a matching value, generating a data structure by organizing and storing: (i) each of the named entities and an assigned class for each of the named entities, (ii) each of the value matches and the token matching each of the value matches, and (iii) the utterance, in a predefined format for the data structure, and outputting the data structure.

The application is suitable for the technical field of computers, and provides a named entity recognition method, including: acquiring a to-be-recognized text, and converting the to-be-recognized text into a first matrix of n*k dimensions; performing multi-layer convolution layer convolution on the first matrix, wherein the last convolution layer of a convolution kernel in the multi-layer convolution layer has a channel number of m, four convolution operations are performed on the last convolution layer, and four parallel second matrices of n*m dimensions are obtained; performing attention weight self-adaption on three second matrices of the four second matrices to obtain a third matrix of n*m dimensions, performing matrix addition on the third matrix and the remaining one second matrix, and outputting a fourth matrix of n*m dimensions; performing classification on the fourth matrix, and outputting an entity label corresponding to the to-be-recognized text; and outputting a named entity corresponding to the to-be-recognized text according to the entity label. By introducing an attention mechanism in the convolution layer, data redundancy is effectively reduced, the number of model parameters is reduced, and the recognition speed of the named entity recognition model is accelerated.

The invention discloses a boundary event aggregation classification algorithm based on space-time correlation analysis. The algorithm comprises the following steps: constructing event classification and a named entity model; calculating association rules between border events based on the historical event topic collection data; constructing a affair graph based on association rules; performing event classification and named entity identification on the border events based on event classification and a named entity model; and based on the event classification and the named entities, classifying and sorting the border events through the event atlas to obtain a border event aggregation result. Aiming at the characteristics of border event data, the invention provides an incidence relation mining algorithm based on the border events, the event atlas of the border events is obtained and is used for incidence aggregation of the events, the border event rules can be effectively extracted, and the events are collected according to themes.

The invention provides a named entity recognition method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an initial text to be subjected to entity recognition processing; obtaining first feature information and second feature information of the initial text based on the initial text and the part-of-speech information of each word in the initial text; and determining the named entity in the initial text according to the first feature information and the second feature information. According to the named entity recognition method provided by the embodiment of the invention, the recognition efficiency of the named entity is improved without depending on prompt construction information input by a user in an auxiliary manner, and the accuracy of obtaining the named entity by subsequent decoding is also ensured by fusing the part-of-speech information of the words in the encoding process.

The invention relates to the technical field of natural language processing, and discloses a named entity recognition method and device and a product, and the method comprises the steps: obtaining a to-be-recognized text sequence; extracting embedding vectors of the text sequence, wherein the embedding vectors comprise a word embedding vector, a character embedding vector, a vocabulary embedding vector and a syntax embedding vector; fusing the word embedding vector, the character embedding vector and the vocabulary embedding vector to obtain a first fusion vector, and extracting a global feature tensor and a local feature tensor of the first fusion vector; performing feature enhancement on the syntactic embedding vector to obtain a deep grammar feature vector; and generating a named entity recognition result of the text sequence based on the global feature tensor, the local feature tensor and the deep grammar feature vector. According to the method, the global feature tensor and the local feature tensor are fused, the context information of the text can be effectively utilized, the deep grammar feature vector is added, the understanding of the grammar structure can be enhanced, and therefore the recognition precision of the named entity is improved.

The invention discloses a named entity recognition model training method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining domain data, and carrying out the data cleaning processing of the domain data, and obtaining sample data; obtaining field information of the sample data, and analyzing and processing the sample data to obtain semantic information of each piece of data in the sample data; performing entity labeling on the sample data according to the set entity type, field information and semantic information to obtain labeled sample data; and training the entity naming recognition model according to the labeled sample data, and obtaining a trained named entity recognition model after the training is completed. According to the technical scheme, the data is distilled through the large-scale language model, the unlabeled data is labeled in a specific scene, then the named entity model is trained, manual labeling is not needed, and the training convenience and accuracy are improved.

The invention provides an entity enhancement and context-aware paragraph retrieval method for an RAG system for solving the problems of fuzzy query intention and insufficient paragraph context modeling in RAG retrieval. The method comprises the following steps: firstly, identifying and extracting a key entity by using a named entity, carrying out weighted fusion on the key entity and question representation obtained by a pre-training model to form an enhanced query vector, and accurately describing a core semantic intention of the question; secondly, performing semantic modeling on paragraphs in a document library, mining a potential semantic association relationship between the paragraphs, and constructing a context interaction model between the paragraphs based on a graph neural network and a gating loop unit mechanism, so as to obtain paragraph vector representation with complete semantics and clear hierarchy; and finally, calculating the similarity between the enhanced query and the paragraph vector, and completing high-precision paragraph-level retrieval. According to the method, the correlation and the recall rate are remarkably improved, insufficient entity utilization and weak context modeling are relieved, and good expansibility and cross-domain applicability are achieved.

Certain aspects of the disclosure concern a computer-implemented method for improved data security in large language models. The method includes receiving a prompt query entered through a user interface, extracting a plurality of named entities from the prompt query and classifying the plurality of named entities into respective entity classes, tagging the plurality of named entities to be security compliant or security noncompliant based on the respective entity classes, and responsive to finding that one or more named entities are tagged to be security noncompliant, generating an alert on the user interface.

The invention discloses a named entity identification method and device, a storage medium and electronic equipment. The method comprises the steps that a prompt word template comprising a definition text and a task instruction text is constructed, the definition text is used for defining an entity to be recognized, and the task instruction text comprises a plurality of sub-task texts arranged according to a preset execution sequence; the plurality of sub-task texts comprise a first sub-task text for performing identification and classification based on the definition text and a second sub-task text for performing post verification on an identification and classification result corresponding to the first sub-task text, obtaining to-be-identified text data, and inputting the cue word template and the text data into a target language model, and identifying and generating the target entity and classification information of the target entity. According to the method and the device, the technical problem of relatively high identification calculation overhead caused by relatively low identification efficiency of the named entity is solved.

The invention relates to the field of artificial intelligence, and discloses a two-segment named entity recognition method and device, equipment and a medium, and the method comprises the steps: obtaining a to-be-recognized original text; a first text is constructed according to the original text, the first text comprises an identification task prompt text, and the identification task prompt text is used for prompting a large language model to execute a named entity identification task on the original text; inputting the first text into a large language model to obtain a named entity recognition result of the original text; a second text is constructed according to the named entity recognition result and the original text, the second text comprises a verification task prompt text, and the verification task prompt text is used for prompting the large language model to execute an entity verification task on the named entity recognition result based on the original text; and inputting the second text into the large language model to obtain a corresponding verification result. According to the two-stage named entity recognition based on the large language model, the recognition efficiency and accuracy are improved, and diversified named entity recognition requirements can be met.

The invention discloses a cross-language named entity recognition method and device based on large interval representation learning and a medium, and the method comprises the steps: obtaining a source language annotated text sequence and a target language unannotated text sequence, and respectively extracting a first span and a second span; and training the source model by using the first span, and generating a pseudo tag corresponding to the second span. And according to the target language span feature and the similarity between the target language span feature and the category center to which the target language span feature belongs, evaluating the false label confidence, and dividing the false label confidence into high confidence and low confidence. And training a target model by using a joint loss function in combination with a source language annotation span and a high-confidence target language pseudo-tag span. And correcting the low-confidence false label to participate in subsequent iterative training of the target model so as to utilize more target language data. The trained target model is used for identifying the named entity in the target language text. The core of the method is to improve the cross-language named entity recognition performance by combining large interval representation learning with pseudo tag screening and dynamic correction.

The invention relates to an industrial named entity recognition method based on privacy protection, and belongs to the technical field of natural language processing, the industrial named entity recognition method is composed of a plurality of local clients and a global model, and the industrial named entity recognition method comprises the following steps: step 1, obtaining an industrial fault detection data set, dividing a training set and distributing labels; 2, establishing an initial model; 3, each local client downloads global model parameters from the server; 4, after each local client sets an iteration round, the private data is used for training in the initial model, and model parameters are uploaded to the server; 5, the server aggregates the model parameters and updates the global model; step 6, repeating the step 3 to the step 5 until a set iteration round is finished; and 7, inputting data needing entity recognition into the trained model, and extracting a test set entity. According to the method, the data privacy of each client is protected while the performance of the local model is improved, and the accuracy of named entity recognition is enhanced.

The invention discloses a knowledge graph construction method based on a sparse scaling network and multiple hypergraphs, and belongs to the technical field of information extraction. Comprising the following steps: firstly, preprocessing texts in a corpus; secondly, performing boundary identification on entities possibly existing in the text, and generating boundary candidate marks of the entities; then, each word or character is labeled, and multiple local hypergraph representations are generated from the front direction, the back direction, the left direction and the right direction; then, decoding the multiple local hypergraphs, and identifying a nested named entity; finally, a multi-layer perceptron-based model is used to learn mapping from entity grammar features to entity pair relationship types. Through redundant information processing based on the sparse scaling network, redundant information can be reduced, and key semantic features in the text can be captured more accurately; through a nested named entity recognition method based on multiple hypergraphs, the performance of knowledge graph construction is improved.

The invention discloses a text named entity extraction method based on channel attention and character features. Comprising the steps of obtaining a to-be-recognized input text; constructing a named entity recognition model, wherein a BERT model is used for obtaining features with context information, a grammar structure and semantic representation; the CharCNN module is used for extracting character-level features in an output result of the BERT model; the DSENet module is used for enhancing entity content and entity position feature expression in an output result of the BERT model; the multi-layer bidirectional long-short-term memory network is used for capturing a long-distance dependency relationship in a fusion result after feature fusion is carried out on the output of the modules; the conditional random field is used for performing entity labeling on the output sequence; and inputting a to-be-recognized input text into the named entity recognition model to obtain a named entity. According to the scheme, the entity recognition effect is improved, and high accuracy and recall rate are achieved in an unstructured text NER task.

In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

A server uses a transformer model to identify a named entity associated with a transaction record. The server receives a transaction record including a text string that includes a non-normalized version of a name of a named entity. The server generates a first embedding of the text string using a first transformer model and identifies a set of similar transactions by comparing the first embedding to second embeddings representing the similar transactions. The server inputs the text string of the transaction record and the set of similar transactions into a second transformer model. The server receives an output from the second transformer and determines that the output indicates that the non-normalized version of the name in the transaction record is classifiable to one of the normalized named entities in the list. The server associates the transaction record with the normalized named entity to which the non-normalized named entity is classifiable.

A method includes identifying, using an automated speech recognition (ASR) system, at least one named entity hypothesis from at least one audio input. The method also can include providing, using the ASR system, the identified at least one named entity to a large language model (LLM). The method also can include generating a prompt using an automated prompt generator. The method also can include processing, using the LLM, the identified at least one named entity hypothesis and the prompt to generate updated named entity recognition data. The method also can include providing the updated named entity recognition data back to the ASR system.