244 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.

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.

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 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 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 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 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 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.

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.

The invention provides a case file named entity recognition method and system based on semantic analysis, and the method comprises the following steps: obtaining original text data of a judicial case electronic file, carrying out the data preprocessing and word level division of the original text data, and obtaining a lexical element sequence; processing the lexical element sequence by adopting a sliding window mechanism, extracting syntactic phrases and fragment feature information, and fusing to generate a candidate fragment feature set; entity types are defined, similarity calculation is carried out on the entity types and the candidate segment feature vectors, the entity type with the highest similarity score is obtained and associated with the candidate segments, and a flat entity set is generated; constructing a predefined structured template, and matching entities into slots of the predefined structured template by adopting a pre-trained classification model to obtain an initial structured instance set; according to the method, the key entities in the file can be efficiently identified, the structural relationship and event information between the entities can be automatically constructed, and the comprehensiveness, accuracy and processing efficiency of case semantic extraction are improved.

The invention discloses an open type named entity recognition method and platform for social media, relates to the technical field of natural language processing, and aims to automatically optimize prompts for a large model based on a Monte Carlo tree search algorithm and reconstruct texts according to the prompts. A social media short text is converted into a low-noise formal text with a standard syntactic structure, and then a concept correction thinking chain template is utilized to extract named entities from a reconstructed text and classify the named entities. The method is used for carrying out open naming body recognition on the disordered social media short text under the condition that fine adjustment is not carried out.

The invention relates to a network security domain knowledge graph construction method. The method comprises the steps of (1) performing named entity extraction for a network security domain based on multi-model cooperative verification, and training a lightweight model; (2) segmenting a long text based on an entity perception multi-dimensional scoring dynamic sliding window; (3) performing named entity and relation extraction and lightweight entity relation identification model construction based on multi-model collaborative network security; and (4) based on the extracted and disambiguated entities and relationships, designing a knowledge graph mode to construct a network security knowledge graph. The invention also relates to a corresponding system, device, processor and computer readable storage medium. By adopting the network security domain knowledge graph construction method, system and device, the processor and the computer readable storage medium, the computing power demand of a large model during element extraction is effectively reduced, and the accuracy and recognition types of network security entities and relationships thereof when the large model processes a long text are improved.

The invention relates to the technical field of data processing, and discloses a policy question and answer method and system based on a knowledge base, and the method comprises the steps: constructing a dynamic graph of a policy provision according to the logic assertion, the policy result and the policy logic intensity in the policy provision; identifying entity attributes in the question through named entities in the policy field; binding the entity attributes to child nodes of the dynamic graph, and executing Boolean truth value verification on the bound child nodes; when the verification result is that the policy provisions accord with the dynamic graph, activating a father node in the dynamic graph, and backtracking the father node in the reverse direction along the dynamic graph to obtain a basis chain of the policy provisions; and converting the logical relationship of the basis chain into a natural language derivation statement, and outputting the natural language derivation statement as an answer to a user side. The quality of answers generated in policy questions and answers can be improved.

The invention discloses a causal knowledge graph construction and question-answering system based on retrieval enhancement generation and a large language model. The system comprises a multi-source heterogeneous knowledge base construction module, a retrieval enhancement generation module, a named entity recognition and causal triple extraction module and a knowledge fusion reasoning module, according to the method, a high-quality named entity annotation data set and a causal data set are constructed, and normalization and integrity of input knowledge are guaranteed; a mixed retrieval strategy (keyword + vector + sparse embedding) is provided, and the evidence coverage rate and recall precision are improved. Under RAG driving, LLM is combined, two rounds of causal triple extraction are achieved, and the causal relationship coverage degree and the direction judgment confidence degree are improved; a conflict detection and atlas fusion mechanism is designed to ensure the unity and consistency of new and old causal knowledge; a question answering system based on a causal atlas is also established, multi-hop causal reasoning is supported, and answers with controllable credibility and explainable are output.

Aspects of the subject disclosure may include, for example, a method including training a first machine learning model to recognize a predetermined named entity in a sentence and a corresponding label, receiving input sentences including a target named entity, receiving a plurality of few-shot examples in a support set, the plurality of few-shot examples including an annotated label for the target named entity, performing annotation on the input sentences with the trained first machine learning model using the plurality of few-shot examples and using no prompt, generating labeled data including the annotated input sentences, performing post-annotation on the labeled data with a second machine learning model that performs confident learning based label noise detection, and generating cleaned labeled data excluding one or more noisy labels from the labeled data. Other embodiments are disclosed.

The application discloses a kind of multi-text feature adapter enhanced professional accomplishment named entity recognition method.The application is according to the meaning of content to professional accomplishment named entity and is labeled, and based on "BIO" method, professional accomplishment named entity is labeled with character tag;And with the characteristics and advantages of BERT model in the field of natural language processing, multi-text feature adapter is integrated into BERT to fine-tune the model, and MFEBERT model is proposed;Professional accomplishment named entity recognition model of MFEBERT+BiLSTM+CRF is constructed, MFEBERT utilizes the multi-text feature adapter to fuse the character-level features, lexical-level and part-of-speech-level fusion features of professional accomplishment named entity, learns the constraint conditions of professional accomplishment named entity by BiLSTM+CRF, and finally realizes the intelligent identification of professional accomplishment named entity, provides important technical support for scientific, accurate and effective construction of professional accomplishment evaluation index system, and helps to promote the innovative construction and application of education evaluation system.

Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.

The present invention relates to the field of spatial positioning data analysis and application technology, and discloses a geographic named entity matching method and electronic device that integrates spatial semantics. The method comprises: inputting target query address information into a multi-target fusion matching model based on spatial semantics, the multi-target fusion matching model being used to generate a similarity score between the target query address information and candidate address information; the multi-target fusion matching model comprising an encoder and a similarity calculation module; the encoder training process comprising: setting multiple decoders corresponding to an encoding network; adjusting the parameters of the encoding network based on the outputs of the multiple decoders to obtain the encoder; the multiple decoders comprising at least two of the following: a geographic named entity feature segmentation decoder, a geographic named entity matching decoder, and a geographic named entity spatial similarity score decoder. This provides a new geographic named entity matching method that integrates spatial semantics.

The application discloses a network security entity identification method and system based on a multi-layer channel attention, and belongs to the technical field of network security entity identification. The specific method comprises the following steps: performing data preprocessing on a real network security entity text data set; constructing a named entity identification model based on a multi-layer channel attention; inputting the preprocessed network security entity text data set into the named entity identification model for model training, so as to obtain a trained named entity identification model; inputting an actual network security entity text data set into the trained named entity identification model, and outputting a label sequence; identifying the entity category of each word in the actual network security entity text data set according to the output label sequence, and realizing network security entity identification based on a multi-layer channel attention. The application can accurately identify various entity types in network security text, and improve the monitoring and analysis capability of network security events.