337 results about "Document structuring" patented technology

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

The invention discloses a multi-modal document structured processing and knowledge extraction method based on a large language model. The method comprises the following steps: S1, receiving an input multi-modal document; s2, performing preprocessing on the text data in the multi-mode document; s3, performing feature extraction on non-text data in the multi-mode document; s4, performing multi-modal data fusion on the preprocessed text data and the non-text data after feature extraction; s5, performing deep semantic analysis on the fused multi-modal data through a pre-trained improved BERT model, wherein the deep semantic analysis comprises key entity recognition, relation extraction and event detection; s6, based on a deep semantic analysis result, automatically constructing a knowledge graph by using the extracted information; and S7, outputting the data of the knowledge graph in a format for analysis or application. According to the method and the system, structured processing and knowledge extraction of unstructured and multi-modal documents are realized.

The invention provides a semi-structured file processing method based on an LLMs (Language Language Model), which is used for efficiently analyzing a semi-structured file and converting the semi-structured file, and belongs to the technical field of intelligent document processing and multi-modal learning. The method is characterized by comprising the following steps: multi-modal fusion analysis: adopting a hierarchical cross-modal attention mechanism HCMA, and combining a multi-head attention mechanism with a graph neural network GNN to realize multi-level feature fusion of a text and an image; adaptive structured conversion: adopting a dynamic structure analysis network DPN, and combining reinforcement learning and a generation model to realize adaptive analysis of a document structure; and model generation and multi-modal reasoning: adopting a multi-modal generation reasoning network MGRN, and combining a diffusion model and neural symbol reasoning to realize joint generation and reasoning of the text and the image. According to the method, a remarkable technical breakthrough is brought to the aspects of document analysis, information extraction and intelligent reasoning, and powerful support is provided for data-driven innovation of various industries.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

According to the knowledge text retrieval method and device, the storage medium and the computer equipment, when knowledge text retrieval is carried out, the retrieval statement and the target knowledge document corresponding to the retrieval statement are obtained firstly, the document structure tree of the target knowledge document is determined, the document structure tree is of a tree structure constructed by utilizing the directory structure of the target knowledge document, and the retrieval statement is stored in the document structure tree; according to the method, a target knowledge document is taken as a component of the target knowledge document, an index structure between each index node in the component of the target knowledge document and the target knowledge document is recorded, and leaf nodes in the index nodes are connected through pointers to form a linked list structure. The corresponding index node can be dynamically retrieved based on the association relationship between the index structure and the linked list structure of the tree structure, and the context content corresponding to the retrieval statement in the target knowledge document is intercepted by adopting different modes based on the type of the index node. And omission of unrelated contents of other chapters and key contents of the same chapter is avoided, so that the retrieval precision and quality are improved.

The invention relates to a knowledge retrieval method, system and device based on document structure context enhancement and a medium, and the method comprises the following steps: obtaining a multi-source heterogeneous structured document, carrying out unified structure analysis and formatting, and extracting metadata and hierarchical structure information of the document; constructing a document directory tree and chapter association according to the extracted document metadata and hierarchical structure information, and segmenting chapters and paragraphs into knowledge blocks; performing hierarchical context enhancement on the knowledge blocks according to the document directory tree to form enhanced knowledge blocks; vectorizing the enhanced knowledge block by using the embedded model, and storing the enhanced knowledge block and a vectorization result thereof into a knowledge base; performing multi-path and multi-layer retrieval in the knowledge base according to a user query text, and recalling related knowledge blocks; and sorting the recalled knowledge blocks, and extracting the most relevant knowledge block to obtain a retrieval result. Compared with the prior art, the method has the advantages that the document retrieval efficiency is improved, and the like.

The invention relates to the technical field of document processing, in particular to a knowledge graph-based document intelligent writing and analysis system, which comprises a graph dynamic updating module, an entity recognition and mapping module, a context connection analysis module, a semantic structure rearrangement module and a semantic coherence verification module. According to the method, the real-time data source is adopted, the knowledge graph is dynamically updated and expanded, synchronization of document content and the current information trend is ensured, noun phrases and entity distribution in a text are accurately analyzed through intelligent entity recognition, the accuracy of information extraction is improved, the coincidence degree of keywords between paragraphs is calculated, and synonymous entities are intelligently inserted, so that the information extraction efficiency is improved. According to the method, the context connection quality of the document is improved, the document structure is automatically rearranged according to the correlation of the content, the logic presentation of the information is optimized, the reading experience is enhanced, and the overall semantic consistency of the document is ensured and the interpretation ambiguity is reduced through complex semantic coherence verification.

The invention discloses a contract filing method, system and device based on automatic recognition and a medium, and the method specifically comprises the steps: taking a contract document uploaded by a user as input, and extracting contract key data; inputting the signature area coordinate, the signature date metadata and the clause keyword into a space-time correlation model to generate a context enhanced candidate contract number set; inputting the candidate contract number set into a preset knowledge graph, and outputting a target contract number; inputting the distribution characteristics of the clause keywords into a bidirectional long-short-term memory network classifier for processing, and constructing a multi-dimensional metadata matrix in combination with the target contract number; and inputting the desensitized contract text content and the multi-dimensional metadata matrix into a file DNA atlas generator, and generating a unique feature code by fusing a semantic embedding vector and a document structure fingerprint. The efficiency bottleneck and accuracy problems of a traditional archiving mode are solved, and the efficiency, accuracy and compliance of contract archiving are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent document processing method, device, equipment and medium, comprising: receiving a document processing request and generating a task identifier, creating and sending a document processing task, constructing an intermediate document structure and generating a plurality of subtasks, and processing the sub-tasks, writing states and page contents into a cache, regularly querying task states, updating accessible identifiers, responding to an access request, loading the page contents, and integrating pages to generate a complete document after all the sub-tasks are completed. According to the method, the document generation task is divided into a plurality of sub-tasks which can be independently processed, and page-level asynchronous loading is realized in combination with a state cache and a regular query mechanism, so that the response speed and the system processing efficiency are remarkably improved while the generation accuracy is ensured; the problems that in the prior art, document generation delay is high, loading is slow, and user waiting time is long are effectively solved.

The embodiment of the invention provides a document-based intelligent question and answer method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: performing document analysis on a target electronic document to obtain document attributes and document contents; based on the document attribute and the document content, document segmentation is conducted on the target electronic document, document segmentation units and unit position information of the document segmentation units are obtained, and the document segmentation units comprise segmentation unit identifiers; based on the document attribute, performing network construction on the target electronic document to obtain a document structure tree network; on the basis of the unit position information, the document segmentation units are arranged in sequence, and a document linear list is obtained; performing document query on the document structure tree network or the document linear list based on the document problem information and the segmentation unit identifier to obtain document query information; and based on the document query information, performing question reply on the document question information. According to the embodiment of the invention, the intelligent question and answer accuracy based on the document can be improved.

The invention discloses an advertisement compliance knowledge question-answering method, device and equipment for foreign enterprises, and the method comprises the steps: obtaining advertisement compliance data of a global data source, carrying out the document structuring and knowledge extraction of the advertisement compliance data, and constructing an advertisement compliance knowledge graph; converting text data in the advertisement compliance knowledge graph into vectors to obtain vector data; semantic analysis is conducted on a query question input by a user, construction is conducted in combination with the vector data, prompt words are obtained, the prompt words are input into a preset large language model, and target answers are obtained; and outputting the target answer based on the advertisement compliance knowledge graph, and visually displaying the target answer in a corresponding answer query graph. And comprehensive and efficient compliance information query and answer services can be provided, so that compliance review requirements of foreign enterprises on advertisements in a multi-language and cross-regional environment can be met.

The invention discloses an intelligent question and answer method and a related device, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a question input by a user and a target document corresponding to the question; the method comprises the following steps: firstly, determining a vector of a problem and vectors of a plurality of text fragments corresponding to a target document, wherein the plurality of text fragments corresponding to the target document are obtained by segmenting the target document from a plurality of granularities based on a document structure; on the basis of the similarity between the vector of the problem and vectors of a plurality of text segments corresponding to the target document, candidate text segments are determined; and finally, based on the candidate text fragments, generating answers corresponding to the questions. According to the method and the device, through the multi-granularity segmentation strategy based on the document structure, the target document can be more efficiently analyzed and analyzed in the subsequent steps on the premise that the integrity of original text information of the target document is not lost, so that information related to the question can be more accurately retrieved, and the accuracy and the reliability of answers are improved.

The invention discloses a page rendering method, a page rendering device, electronic equipment and a readable storage medium, the method comprises the steps that a document object model tree is constructed by analyzing a source code of a target page, and each HTML tag in the source code corresponds to an object node in the document object model tree; a document structure tree is constructed by analyzing the document object model tree, the document structure tree comprises document nodes and partition nodes, each partition node comprises at least one paragraph node, each paragraph node comprises at least one content node, and each content node comprises at least one content node; the document node, the partition node, the paragraph node and the content node all comprise node control information; and performing page rendering according to the document structure tree.

Systems and methods are disclosed for training an electronic document extraction model, including the generation of the training data to train the model based on sampling a pool of electronic documents based on a rareness metric of the documents. Each electronic document has a document-level rareness metric generated, with the document-level rareness metric being based on one or more of a structural rareness metric or a content rareness metric of the document. The structural rareness metric measures the rareness of the document structure, which may be irrespective of the text content of the document. The content rareness metric measures the rareness of the document content, which may be irrespective of the document structure. The electronic documents are sampled based on the document-level rareness metrics to increase the number of rare documents in the training data without unduly biasing the sampling to optimize the training data for training the extraction model.

A system may receive from a client machine a request to determine a structured document based on an exemplar document and a natural language input instruction characterizing content to include in the structured document. The system may determine document structure information based on an analysis of the exemplar document. The document structure information may identify a plurality of document sections and may characterize content to include in the plurality of document sections. The system may query a database system to identify a plurality of example document portions corresponding to the plurality of document sections. The system may determine a structured document including a plurality of novel text portions corresponding with the plurality of document sections. The novel text portions may be generated by a large language model based on the plurality of example document portions and the natural language input instruction.

The invention discloses an LLM-based document structuring automatic processing method and system, and relates to the technical field of document processing, and the method comprises the following steps: S1, document input and preprocessing: removing special characters in a disordered text, unifying a coding format, and adjusting the semantic density; s2, redundancy removal and keyword extraction: removing repeated information in the disordered text, and extracting a first keyword; s3, semantic extraction and structuring: parsing the disordered text sentence by sentence according to the first keyword, and extracting main meaning and related semantic information corresponding to each sentence to obtain an extraction result which comprises a second keyword and sentence meaning of the same sentence; s4, constructing and updating a semantic stack: constructing and updating the semantic stack according to an extraction result, and storing a semantic unit; one semantic unit corresponds to the same extraction result; and S5, structured output: outputting the data in the semantic stack in a preset format. The method is suitable for structured processing of complex disordered documents in multiple fields and multiple types.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based multi-modal document analysis system, which consists of an image preprocessing module, a multi-modal feature extraction module, a natural language processing module, a modal fusion and analysis module and a document structure identification module. According to the method, the image quality is improved through image sharpening and noise and blurring removal, text, table and image areas are extracted through masks respectively, different document contents are separated accurately, visual features and extracted area information are combined together, a comprehensive feature vector is generated, the document contents are understood more comprehensively, and the document quality is improved. According to the method, the text extracted from the image is analyzed, semantic analysis and keyword extraction are carried out, so that text content is understood, keywords are extracted from the analyzed text, main topics and key points of the document are recognized, and the beneficial effect that the analysis result is more accurate due to multi-modal analysis of the document is achieved.

The invention provides an enhanced retrieval generation method and device based on a document structure knowledge graph. The method comprises the following steps: analyzing a document to obtain document analysis content; according to the document analysis content, constructing document entities and text fragment entities, and establishing association relationships between the document entities and the text fragment entities and between the text fragment entities; constructing a knowledge graph of the document structure; the knowledge graph is stored in a graph database, text content embedding processing is conducted on text fragment entities in the knowledge graph, and vector representation of the text content is generated; in response to a query request of a user, retrieving a text fragment entity related to the query request from the knowledge graph, and generating a corresponding query result according to the text content of the retrieved text fragment entity; and processing the query result by using a preset language model to generate an answer corresponding to the query request. The reasoning ability can be improved, and the traceability of the generated result and the accuracy of the answer can be improved.

The invention discloses an intelligent document generation system and method for bidding documents, and the system comprises a preprocessing module which is used for obtaining bidding document files in various formats, and converting the obtained bidding document files in various formats into structured bidding document files; the analysis module is used for analyzing the converted bidding document file based on the large model cue word to obtain a structured bidding document file analysis result; the directory generation module is used for generating a directory of the bidding document file according to the analysis result; the retrieval module is used for generating a corresponding knowledge content set according to the bidding document file directory; and the content generation module is used for generating the text content of the bidding document file based on the knowledge content set and the structure information of the bidding document file directory. And the content assembly module is used for performing multi-dimensional feature extraction and classification on the generated bidding document file text content, and completing content assembly according to a preset document structure tree to obtain a standard document meeting bidding document requirements.

The invention provides a large model retrieval question and answer method and device based on a document structure tree, and the method is characterized in that the method comprises the following steps: S1, respectively analyzing each document, and respectively obtaining a corresponding business tree; s2, for each service tree, converting each complete path from a root node to a leaf node in the service tree into a corresponding path vector; s3, obtaining a candidate node set according to the user problem and all the path vectors in combination with a vector model; s4, judging whether an answer is generated according to the candidate node set or not, if yes, generating the answer according to the large language model and the candidate node set, and if not, executing the step S5; s5, performing hierarchical matching on all the service trees through a large language model according to the user question to obtain a hierarchical matching result; and S6, generating an answer through a large language model in combination with a hierarchical matching result. In short, the method can realize accurate, efficient and low-cost question reply.

The invention discloses a multi-modal document structured processing method and device, equipment and a medium. For other different data types extracted from the to-be-processed document, the data of the other data types can be subjected to structured processing according to the preset structured processing steps corresponding to the other data types, so that the information carried by the data of the other data types is deeply mined, and the processing efficiency of the to-be-processed document is improved. And the information carried by the data of other data types in the to-be-processed document is presented in a unified and structured form through effective integration and utilization of the multi-modal data. For the image data, the character image type and the non-character image type are distinguished, and different processing modes are adopted, so that the information carried in the image data is extracted more accurately. For the table data, the table data is converted into an information set with rich semantics from a simple numerical matrix by obtaining a header name and a line name and generating a descriptive text for each data item.

The embodiment of the invention provides a document abstract extraction method and system, electronic equipment and a storage medium, and belongs to the technical field of navigation. The method comprises the following steps: analyzing a document of which the abstract is to be extracted into fragments, and generating a fragment set; performing optical character recognition to form a preliminary semantic text, and determining a first token sequence; performing image type identification on the illustrations in the fragment set to form a structure description text, and determining a second token sequence; identifying an industry or field label of the document of which the abstract is to be extracted, and determining a corresponding code; and inputting the first token sequence, the second token sequence and the code corresponding to the tag into a pre-constructed abstract extraction model to obtain an abstract text. According to the image-text document abstract generation scheme based on the recurrent neural network, key semantics and key illustrations are extracted from enterprise documents through key semantics extraction, document structure extraction and document industry or field recognition, image-text document abstracts are formed, and the image-text document abstract generation scheme has pertinence and is high in generalization capacity.

A method includes obtaining a document structure from a data repository. The document structure includes multiple structured sections. A table is detected in a first structured section. A table representation of the table is processed by a general large language model (LLM) to generate a natural language description of the table. An image is detected in the first structured section. The image is processed by an image-processing LLM to generate a natural language description of the image. A form is detected in the first structured section. The form is processed by the general LLM to generate a natural language description of the form. The natural language descriptions of the table, image and form are inserted into the first structured section to obtain a modified first structured section. A modified document structure including the modified first structured section is outputted.

The invention discloses a PDF (Portable Document Format) document structured extraction system based on a multi-modal language model, belongs to the technical field of document processing and optical character recognition, and aims at solving the technical problem of how to improve the existing OCR (Optical Character Recognition) technology to improve the analysis capability of a complex document structure and improve the recognition precision of handwritten forms and other non-standard fonts. According to the technical scheme, the system adopts a layered decoupling architecture and comprises an input layer, a preprocessing layer, a reasoning layer, an output layer and a monitoring and fault-tolerant module; wherein the output layer is used for multi-source data access and path management to realize a local file system or S3 cloud storage; the preprocessing layer is used for invalid document filtering and visual feature extraction; the reasoning layer is used for multi-modal model interaction and content processing; the output layer is used for outputting a content aggregation result; and the monitoring and fault-tolerant module is used for realizing real-time state monitoring, resource consumption analysis and exception handling.

The invention provides a PDF (Portable Document Format) document structured loading method based on image recognition, which relates to the technical field of image data processing, and comprises the following steps: setting a predetermined extraction scale of a document image based on margin information of a PDF document; obtaining an initial image of the PDF document with a predetermined coarse scale in predetermined extraction scales; introducing a preprocessing strategy to process the initial image to obtain a target image; a target feature parameter set of the target image is collected in a multi-dimensional mode, the loading engine classifier is activated to analyze the target feature parameter set, and a target engine category is determined; and carrying out structured loading on the initial image through the target engine category. The technical problem that in the prior art, due to the fact that a loading engine cannot be adaptively selected according to semantic features and layout forms of images in a PDF document, the image type PDF structure restoration effect is poor is solved, and the technical effect of improving complex PDF document structure reconstruction accuracy and data loading quality is achieved.

The invention provides a permission configuration method and device. The method comprises the following steps: acquiring a source code and relative path information of a target document edited in a current editing environment; wherein the relative path information is used for representing position information of the target document relative to a root directory of a working area of the current editing environment; analyzing the source code to obtain a document structure object of the target document; performing node traversal on the document structure object to identify a target node corresponding to the interaction component from each node of the document structure object; according to the permission attribute corresponding to the target node and the relative path information, generating an attribute value of the permission attribute in the source code; wherein the attribute value is used for performing permission configuration of the interaction component, so that the accuracy and consistency of permission configuration are improved, the workload of manual permission configuration of developers is reduced, and the development efficiency is improved.

The invention discloses a document content generation method based on a title structuring demand and related equipment, and relates to the technical field of artificial intelligence, the method comprises the following steps: performing structured analysis on a target electronic document to extract at least one title element in the target electronic document; receiving personalized requirements input by a user for the title elements, and performing data association on the personalized requirements and the corresponding title elements; the personalized requirements are used for guiding content generation; the title elements and the personalized requirements corresponding to the data association are combined into a generation request, and the generation request is sent to an artificial intelligence model; using an artificial intelligence model to obtain generation content according to the generation request; and inserting the generated content into a position related to the corresponding title element in the target electronic document. According to the method, the document structure can be deeply understood, a user is allowed to set independent generation requirements for different title elements, a document processing scheme for batch generation and selective optimization can be realized, and the flexibility of document processing is greatly improved.

The invention discloses a document compliance detection method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the document compliance detection method comprises the steps: obtaining a product demand document, converting the product demand document into a to-be-recognized text in a markup language format, and storing the to-be-recognized text in the markup language format; the to-be-recognized text and the compliance rule are input into a pre-trained recognition model, the recognition model outputs a compliance recognition result of the product requirement document based on a content recognition result of the to-be-recognized text and the compliance rule, the compliance rule is used for indicating a corresponding relation between the content recognition result and the compliance recognition result, and the content recognition result is used for recognizing the product requirement document. The compliance identification result comprises compliance and a non-compliance reason under a non-compliance condition; the method not only improves the detection efficiency of the document compliance, but also improves the detection accuracy when processing complex document structures and contents.

Certain aspects of the disclosure provide for information retrieval that exploits context derived from document structure. Source documents can be preprocessed to identify fields and determine context attributes related to each field based on the structural layout of a source document. Resource documents can also be preprocessed to segment a resource document into passages and determine context related to the passages based on structural layout. Queries pertaining to a field can be enhanced by adding context metadata associated with the field. A query embedding can be generated and compared with previously generated passage embeddings to locate candidate matches based on similarity. A machine learning model can be provided with the top-ranked passages and tasked with re-ranking the passages based on relevancy to the original query. The highest re-ranked passage or set of passages can be output in response to the query.