Structured document retrieval processing method based on document vectorization enhancement
The method enhances document retrieval by integrating structured information and improved vectorization, addressing the limitations of keyword-based methods by improving accuracy and efficiency in structured document retrieval, with adaptability and personalization.
Patent Information
- Application Number
- CN202510407512.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-02
- Publication Date
- 2025-07-15
AI Technical Summary
When processing structured documents, the prior art ignores the utilization of structured information, resulting in poor retrieval results and the inability to fully play the role of metadata information.
Using a method based on document vectorization enhancement, through preprocessing, structured information extraction, vectorization, index construction and feedback optimization, combining structured information and document content, more accurate document representation is generated, and searched through inverted indexing and machine learning algorithms, supporting multi-language environments.
It significantly improves the accuracy and efficiency of structured document retrieval, provides personalized search services, adapts to the expansion of different types of document libraries, and adapts to user needs and preferences through user feedback optimization algorithms.
Smart Images
Figure CN120316072A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of big data information retrieval, and particularly to a structured document retrieval processing method based on enhanced document vectorization. Background Art
[0002] With the advent of the big data era, information retrieval technology has become increasingly important in various fields. Traditional information retrieval methods mainly rely on keyword matching, but this method has many limitations in processing structured documents. Structured documents usually contain rich metadata information, such as titles, authors, dates, etc., and these information are crucial for improving the accuracy and efficiency of retrieval. Therefore, how to effectively utilize this structured information to improve the retrieval performance has become an urgent problem to be solved.
[0003] In the prior art, some methods attempt to improve the retrieval effect by enhancing the vector representation of documents. For example, by introducing word embedding technology, the words in the document are converted into vector form to capture the semantic relationships between words. However, these methods often ignore the utilization of structured information or do not fully consider the impact of structured information on the retrieval performance during the vectorization process.
[0004] Therefore, we propose a structured document retrieval processing method based on enhanced document vectorization. Summary of the Invention
[0005] The present invention mainly solves the technical problems existing in the above-mentioned prior art, and provides a structured document retrieval processing method based on enhanced document vectorization.
[0006] In order to achieve the above object, the present invention adopts the following technical solutions. A structured document retrieval processing method based on enhanced document vectorization specifically includes the following steps:
[0007] The first step: Document preprocessing: Preprocess the input structured document to ensure the quality and consistency of the document content;
[0008] The second step: Structured information extraction: Extract structured information from the preprocessed document and combine this information with the document content;
[0009] The third step: Document vectorization: Use an improved vectorization technology to convert the document content and structured information into vector form. This vectorization technology can fully consider the impact of structured information on the document semantics, thereby generating a more accurate document representation;
[0010] The fourth step: Index construction: Based on the vectorized documents, construct an efficient retrieval index for fast retrieval and matching;
[0011] Step 5: Retrieval Processing: According to the user's query request, use the constructed index to retrieve documents, and perform relevance ranking based on the vector representation and structured information of the documents, and finally return the most relevant document results;
[0012] Step 6: Feedback Optimization: Users can evaluate the retrieval results, and the system optimizes the retrieval algorithm according to the user's feedback to achieve the purpose of continuously improving the retrieval effect.
[0013] Preferably, the preprocessing in the first step includes removing useless information, standardization processing, language recognition, and text cleaning. Removing useless information includes, but is not limited to, deleting blank characters, punctuation marks, and stop words. Standardization processing involves converting the text into a unified format, such as unifying the date format and number format. Language recognition is used to determine the language type of the document so that the corresponding language model can be adopted during subsequent processing. Text cleaning includes correcting spelling mistakes and unifying synonyms to improve the accuracy and consistency of the document content.
[0014] Preferably, the structured information in the second step includes the title, author, and date. These information help to quickly locate and identify the document content. At the same time, during the vectorization process, these structured information will be given higher weights to ensure that their importance in the document representation is reflected.
[0015] Preferably, the improved vectorization technology in the third step includes using a word embedding model and a structured information fusion algorithm. The word embedding model can capture the semantic relationships between words, while the structured information fusion algorithm ensures that the structured information is properly weighted and fused during the vectorization process, so that the finally generated document vector can more comprehensively reflect the document content and structural characteristics.
[0016] Preferably, the efficient retrieval index constructed in the fourth step adopts the inverted index technology, which can quickly locate the documents containing specific keywords. At the same time, combined with the index of structured information, the retrieval process is not limited to the text content, but also includes matching the structured attributes of the documents.
[0017] Preferably, the relevance ranking in the fifth step adopts a machine learning algorithm, which learns the relevance between document features and user queries through a training dataset, so as to improve the accuracy and relevance of the retrieval results.
[0018] Preferably, the feedback optimization in the sixth step includes user evaluation collection and algorithm adaptive adjustment. User evaluation collection means that after the user receives the retrieval results, the system provides an interface for the user to rate the relevance and satisfaction of the results. These rating data will be collected by the system and used for subsequent algorithm optimization. Algorithm adaptive adjustment means that the system dynamically adjusts the parameters of the retrieval algorithm according to the collected user feedback data, such as adjusting the weights of the word embedding model and the fusion strategy of the structured information fusion algorithm, so as to continuously improve the retrieval effect. In this way, the system can continuously learn and adapt to the user's retrieval habits and preferences, and thus provide a more personalized retrieval service.
[0019] Preferably, the structured information extraction in the second step further includes metadata extraction and content summary generation. Metadata extraction means extracting information such as keywords, abstracts, and classification metadata from the document, which helps to quickly understand the main idea and content of the document. Content summary generation means that the system automatically generates a summary from the document content to provide a basis for the user to quickly browse and judge the relevance of the document.
[0020] Preferably, the index construction in the fourth step further includes multi-level index optimization. Multi-level index optimization means that the system not only constructs an index based on the document content, but also constructs an index based on the structured information of the document, such as author index and date index. Through this multi-level index structure, the system can achieve faster and more accurate document retrieval.
[0021] The present invention provides a structured document retrieval processing method based on document vectorization enhancement, which has the following beneficial effects:
[0022] 1. This structured document retrieval processing method based on document vectorization enhancement significantly improves the accuracy and efficiency of retrieval by comprehensively considering the document content and structured information. At the same time, it has good scalability and adaptability, supports multi-language document retrieval, provides a more efficient, accurate and personalized retrieval service for users, can effectively utilize the structured information, improve the accuracy and efficiency of retrieval, and meet the requirements of information retrieval technology in the big data era.
[0023] 2. This structured document retrieval processing method based on document vectorization enhancement enables the retrieval system to more accurately understand the user's query intention and quickly locate the most relevant documents by combining the vectorized representation of structured information and document content. This not only reduces the time for users to find the required information in a large number of documents, but also improves the relevance of the retrieval results, thus enhancing the user experience.
[0024] 3. The structured document retrieval processing method based on enhanced document vectorization can adapt to different types of structured documents and maintain high retrieval performance as the document library expands by adopting improved vectorization techniques and multi-level index optimization. In addition, the system can also make adaptive adjustments according to user feedback and continuously optimize the retrieval algorithm to meet the changing retrieval needs and preferences of users.
[0025] 4. The structured document retrieval processing method based on enhanced document vectorization can automatically identify the language type of the document and process it using the corresponding language model by including language recognition in the preprocessing step. This makes the method applicable not only to a single language environment but also to multi-language document libraries, providing consistent retrieval services for global users.
[0026] 5. The structured document retrieval processing method based on enhanced document vectorization particularly considers the semantic information of the document in the vectorized representation of the document content. In the vectorization process, in addition to the traditional word frequency statistics method, semantic analysis techniques such as topic models and semantic encoders in deep learning are introduced. These techniques can capture the implicit semantic information in the document, so that the document vector not only contains the surface information of the vocabulary but also can reflect the deep semantic features of the document. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0028] Embodiment 1: A structured document retrieval processing method based on enhanced document vectorization, as Figure 1As shown in the figure, it specifically includes the following steps: The first step: Document preprocessing: Preprocess the input structured document to ensure the quality and consistency of the document content; The second step: Structured information extraction: Extract structured information from the preprocessed document and combine this information with the document content; The third step: Document vectorization: Use an improved vectorization technique to convert the document content and structured information into vector form. This vectorization technique can fully consider the impact of structured information on the document semantics, thereby generating a more accurate document representation; The fourth step: Index construction: Based on the vectorized documents, construct an efficient retrieval index for fast retrieval and matching; The fifth step: Retrieval processing: According to the user's query request, use the constructed index to retrieve documents, and perform relevance ranking based on the vector representation and structured information of the documents, and finally return the most relevant document results; The sixth step: Feedback optimization: Users can evaluate the retrieval results, and the system optimizes the retrieval algorithm according to the user's feedback to achieve the purpose of continuously improving the retrieval effect. In the first step, the preprocessing includes removing useless information, standardization processing, language recognition, and text cleaning. Removing useless information includes, but is not limited to, deleting blank characters, punctuation marks, and stop words. Standardization processing involves converting the text into a unified format, such as unifying the date format and number format. Language recognition is used to determine the language type of the document for subsequent processing using the corresponding language model. Text cleaning includes correcting spelling mistakes and unifying synonyms to improve the accuracy and consistency of the document content. By comprehensively considering the document content and structured information, the accuracy and efficiency of retrieval are significantly improved. At the same time, it has good scalability and adaptability, and supports multi-language document retrieval, providing users with a more efficient, accurate, and personalized retrieval service. It can effectively utilize structured information to improve the accuracy and efficiency of retrieval and meet the requirements of information retrieval technology in the big data era.
[0029] Embodiment 2: On the basis of Embodiment 1, as Figure 1 shown, the structured information in the second step includes the title, author, and date. These information help to quickly locate and identify the document content. At the same time, during the vectorization process, these structured information will be given higher weights to ensure that their importance in the document representation is reflected. The structured information extraction in the second step also includes metadata extraction and content summary generation. Metadata extraction refers to extracting information such as keywords, abstracts, and classification metadata from the document. These information help to quickly understand the main idea and content of the document. Content summary generation means that the system automatically generates a summary from the document content to provide a basis for users to quickly browse and judge the relevance of the document. By combining the structured information and the vectorized representation of the document content, the retrieval system can more accurately understand the user's query intention and quickly locate the most relevant document. This not only reduces the time for users to find the required information in a large number of documents but also improves the relevance of the retrieval results, thus enhancing the user experience.
[0030] Example 3: Based on Example 1 and Example 2, as Figure 1 shown, the improved vectorization technique in the third step includes using a word embedding model and a structured information fusion algorithm. The word embedding model can capture the semantic relationships between words, while the structured information fusion algorithm ensures proper weight assignment and fusion of structured information during the vectorization process, so that the finally generated document vector can more comprehensively reflect the content and structural features of the document. By adopting the improved vectorization technique and multi-level index optimization, this method can adapt to different types of structured documents and maintain high retrieval performance as the document library expands. In addition, the system can also perform adaptive adjustment according to user feedback and continuously optimize the retrieval algorithm to adapt to the changing retrieval needs and preferences of users.
[0031] Example 4: Based on Example 1, Example 2 and Example 3, as Figure 1 shown, the efficient retrieval index constructed in the fourth step adopts the inverted index technique, which can quickly locate the documents containing specific keywords. At the same time, combined with the index of structured information, the retrieval process is not limited to the text content but also includes matching the structured attributes of the document. The index construction in the fourth step also includes multi-level index optimization, which means that the system constructs not only an index based on the document content but also an index based on the structured information of the document, such as the author index and the date index. Through this multi-level index structure, the system can achieve faster and more accurate document retrieval. In the fifth step, the relevance ranking adopts a machine learning algorithm, which learns the relevance between document features and user queries through a training data set, so as to improve the accuracy and relevance of the retrieval results. By including language recognition in the preprocessing step, the system can automatically identify the language type of the document and process it using the corresponding language model, which makes this method applicable not only to a single language environment but also to multi-language document libraries, providing consistent retrieval services for global users.
[0032] Example 5: Based on Example 1, Example 2, Example 3 and Example 4, as Figure 1As shown, the feedback optimization in the sixth step includes user evaluation collection and algorithm adaptive adjustment. User evaluation collection means that after the user receives the retrieval results, the system provides an interface for the user to rate the relevance and satisfaction of the results. These rating data will be collected by the system and used for subsequent algorithm optimization. Algorithm adaptive adjustment means that the system dynamically adjusts the parameters of the retrieval algorithm according to the collected user feedback data. For example, it adjusts the weights of the word embedding model and the fusion strategy of the structured information fusion algorithm to continuously improve the retrieval effect. In this way, the system can continuously learn and adapt to the user's retrieval habits and preferences, so as to provide more personalized retrieval services. In the vectorized representation of the document content, the semantic information of the document is particularly considered. During the vectorization process, in addition to the traditional word frequency statistics method, semantic analysis technologies such as topic models and semantic encoders in deep learning are introduced. These technologies can capture the implicit semantic information in the document, so that the document vector not only contains the surface information of the vocabulary, but also can reflect the deep semantic features of the document.
[0033] The working principle of the present invention: First, in the document preprocessing stage, the original document is cleaned and standardized to ensure the accuracy and efficiency of subsequent processing. Then, the structured information in the document, such as the title, author, and date, is extracted and given higher weights to highlight its importance during the vectorization process. Next, an improved vectorization technology is adopted, combining the word embedding model and the structured information fusion algorithm, to generate a document vector that can comprehensively reflect the content and structure characteristics of the document. When constructing an efficient retrieval index, the inverted index technology is adopted and combined with multi-level index optimization to achieve fast and accurate document retrieval. In the retrieval processing stage, the constructed index is used for document retrieval, and the relevance ranking is performed through machine learning algorithms to return the most relevant document results. Finally, through user evaluation collection and algorithm adaptive adjustment, feedback optimization is achieved, and the retrieval algorithm is continuously improved to adapt to the changing retrieval needs and preferences of users. The key of the present invention lies in combining structured information with document content. The document vector generated by the vectorization technology can more comprehensively reflect the semantic information and structure characteristics of the document. In addition, the present invention also supports multi-language document retrieval. Through language recognition technology, the language type of the document is automatically recognized, and the corresponding language model is used for processing. This makes the present invention not only applicable to a single language environment, but also able to process multi-language document libraries, providing consistent retrieval services for global users. Through continuous feedback optimization, the present invention can continuously learn and adapt to the user's retrieval habits and preferences, so as to provide more personalized retrieval services.
[0034] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.
Claims
1. A structured document retrieval processing method based on enhanced document vectorization, characterized in that, Specifically, it includes the following steps: The first step: Document preprocessing: Preprocess the input structured document to ensure the quality and consistency of the document content; The second step: Structured information extraction: Extract structured information from the preprocessed document and combine this information with the document content; The third step: Document vectorization: Use improved vectorization techniques to convert the document content and structured information into vector form. This vectorization technique can fully consider the impact of structured information on the document semantics, thereby generating a more accurate document representation; The fourth step: Index construction: Based on the vectorized documents, construct an efficient retrieval index for fast retrieval and matching; The fifth step: Retrieval processing: According to the user's query request, use the constructed index to retrieve documents and perform relevance ranking based on the vector representation and structured information of the documents, and finally return the most relevant document results; The sixth step: Feedback optimization: The user can evaluate the retrieval results, and the system optimizes the retrieval algorithm according to the user's feedback to achieve the purpose of continuously improving the retrieval effect.
2. The structured document retrieval processing method based on document vectorization enhancement according to claim 1, wherein: In the first step, the preprocessing includes removing useless information, standardization processing, language recognition, and text cleaning. Removing useless information includes, but is not limited to, deleting blank characters, punctuation marks, and stop words. Standardization processing involves converting the text into a unified format, such as unifying the date format and number format. Text cleaning includes correcting spelling mistakes and unifying synonyms.
3. The structured document retrieval processing method based on document vectorization enhancement according to claim 1, characterized in that: In the second step, the structured information includes the title, author, and date.
4. The structured document retrieval processing method based on document vectorization enhancement according to claim 1, characterized in that: In the third step, the improved vectorization technique includes using a word embedding model and a structured information fusion algorithm.
5. The structured document retrieval processing method based on document vectorization enhancement according to claim 1, wherein: In the fourth step, the constructed efficient retrieval index uses the inverted index technique.
6. The method for processing structured document retrieval enhanced based on document vectorization according to claim 1, wherein: In the fifth step, the relevance ranking uses a machine learning algorithm.
7. The structured document retrieval processing method based on document vectorization enhancement according to claim 1, wherein: In the sixth step, the feedback optimization includes user evaluation collection and algorithm adaptive adjustment.
8. The structured document retrieval processing method based on document vectorization enhancement according to claim 1, characterized in that: In the second step, the structured information extraction also includes metadata extraction and content summary generation.
9. The method for processing structured document retrieval enhanced based on document vectorization according to claim 1, wherein: In the fourth step, the index construction also includes multi-level index optimization.