Semantic Vector Extraction Model for Text Accuracy
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Solution Overview
Problem
Current information extraction methods struggle to accurately identify and enhance core fragments in text, leading to ambiguity and inaccuracy in automatic style enhancement for applications like content abstract extraction and advertisement optimization.
Innovation Solution
The method involves acquiring a semantic vector of the input text using models like BERT, inputting it to a pre-trained extraction model, and performing boundary correction based on word segmentation to obtain a target enhanced text with improved accuracy and context relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional information extraction methods are used, then the extraction process is simple, but the accuracy of identifying and enhancing core fragments is low
Solution Approach 1:
The extraction model is segmented into multiple functional components: a semantic vector generation module that transforms input text into semantic vectors, an extraction module that identifies core fragments based on semantic vectors, and a boundary correction module that refines extraction boundaries. This segmentation allows each component to specialize in specific tasks, improving overall extraction accuracy while managing complexity through modular design.
Solution Approach 2:
Semantic vectors are introduced as an intermediary representation between raw text and extraction results. The semantic vector generation module transforms input text into dense semantic vectors that capture contextual meaning, which then guide the extraction process. This intermediary layer enables the model to understand semantic relationships and improve extraction accuracy without directly processing raw text in the extraction module.
2Reliability
If semantic vectors are dynamically generated for each input text, then the context relevance is improved, but the computational time increases
Solution Approach 1:
The semantic vector generation module pre-processes the input text by generating semantic vectors before the extraction process begins. This preliminary action transforms raw text into a compact semantic representation that captures essential contextual information, reducing the computational burden during the actual extraction and boundary correction phases.
Solution Approach 2:
The model changes the parameter representation from raw text tokens to dense semantic vectors. This parameter transformation compresses textual information into a lower-dimensional space that preserves semantic relationships, enabling faster processing during extraction while maintaining high context relevance. The semantic vectors serve as an efficient intermediate representation that balances computational efficiency with contextual accuracy.
3Manufacturing precision
If boundary correction based on word segmentation is applied, then the extraction precision is improved, but the processing complexity increases
Solution Approach 1:
The boundary correction module implements a feedback mechanism where extraction results are refined based on word segmentation information. After the extraction module identifies core fragments using semantic vectors, the boundary correction module uses word segmentation boundaries as feedback to adjust and refine the extraction boundaries, ensuring they align with meaningful linguistic units. This feedback loop improves extraction precision by correcting boundary errors.
Solution Approach 2:
The boundary correction process is dynamic and adaptive, adjusting extraction boundaries based on the interaction between semantic vector guidance and word segmentation constraints. Rather than using fixed boundary rules, the model dynamically refines boundaries by considering both the semantic importance identified by the extraction module and the linguistic structure provided by word segmentation, allowing flexible adaptation to different text contexts.
Data Source
AI summary
An information extraction method, an extraction model training method, an apparatus and an electronic device all relate to knowledge graphs. A specific implementation includes acquiring an input text and determining a semantic vector of the input text according to the input text. Such implementation also includes inputting the semantic vector of the input text to a pre-acquired extraction model to obtain a first enhanced text of the input text. The first enhanced text is a text with a text score greater than a preset threshold output by the extraction model. The extraction model performs text extraction based on the semantic vector of the input text. Since the semantic vector has rich context semantics, the enhanced text extracted by the extraction model can be more in line with the context of the input text.