Multi-level text understanding test method and platform based on semantic embedding

Abstract

The invention relates to the technical field of natural language processing, in particular to a multi-level text understanding test method and platform based on semantic embedding, and the method comprises the steps: constructing a semantic embedding module, a multi-level analysis framework and a dynamic evaluation index set, and extracting quantitative model performance through features of a grammar layer, a semantic layer and a context association layer, and a security test and a cross-modal support capability are introduced. According to the method, grammar, semantic and context information can be effectively integrated, the semantic understanding precision and robustness of the model in a complex scene are improved, and a systematic solution is provided for text understanding model optimization.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of natural language processing and artificial intelligence, and specifically relates to a multi-level text understanding test method and platform based on semantic embedding. BACKGROUND

[0002] In the field of natural language processing, the improvement of text understanding ability depends on the deep mining and multi-level analysis of semantic information. In traditional methods, text understanding models are usually based on single-level feature extraction. This design may be limited by the accuracy and breadth of semantic representation in complex scenarios, especially in the comprehensive use of grammar, semantics and context association. Therefore, the existing technology needs a method that can comprehensively evaluate the semantic understanding ability of the model to support more efficient task optimization.

[0003] In recent years, semantic embedding technology based on pre-trained large models has gradually become the core tool of text understanding. This technology can effectively capture deep semantic features by mapping text into high-dimensional semantic vectors, and combine multi-level analysis modules to realize joint analysis of grammar, semantics and context association. This method not only improves the performance of the model in complex tasks, but also provides a basis for the design of dynamic evaluation indicators. Dynamic evaluation indicators can quantify the performance of the model in accuracy, logical consistency and context association ability, helping to identify potential defects and propose optimization strategies.

[0004] The existing text understanding test method faces the following main problems in practical application: Limitations of grammar and semantic analysis: traditional methods may have errors in tasks such as part-of-speech tagging, dependency syntax analysis and entity recognition, and these errors will be passed down layer by layer, affecting the final understanding effect.

[0005] Deficiency of context association modeling: the modeling of coherence of the article puts forward higher requirements for the understanding ability of long text, and the existing method still has room for improvement in long-range dependency processing.

[0006] The lack of cross-modal and robustness testing: the fusion of multi-modal data and the robustness testing of adversarial samples have not been fully incorporated into the evaluation system, which limits the adaptability of the model in complex scenarios.

[0007] The present application realizes comprehensive analysis from the grammar layer to the context association layer by constructing a multi-level text understanding test method based on semantic embedding, and introduces a set of dynamic evaluation indicators and a security testing module, further improving the explainability and anti-interference ability of the model. This method provides a systematic solution for performance optimization of text understanding models. SUMMARY

[0008] The purpose of the present application is to provide a multi-level text understanding test method and platform based on semantic embedding, which solves the problem that syntax, semantics and context-related information cannot be effectively integrated in traditional methods by constructing a semantic embedding module, a multi-level analysis framework and a dynamic evaluation index set, and introduces a security test module and cross-modal support capability to improve the semantic understanding accuracy and robustness of the model in complex scenarios.

[0009] To achieve the above-mentioned purpose, the present application provides a multi-level text understanding test method based on semantic embedding, comprising the following steps: Step (a), constructing a semantic embedding module based on a pre-trained large model, mapping the input text into a multi-dimensional semantic vector; Step (b), multi-level semantic analysis of the text, covering feature extraction of the syntax layer, semantic layer and context-related layer; Step (c), designing a dynamic evaluation index set, combining the semantic vector and the multi-level features to quantify the accuracy, logical consistency and context-related ability of the model in the text understanding task; Step (d), generating a test report to feedback the potential defects of the model and propose optimization strategies.

[0010] Preferably, the step (a) specifically comprises: A semantic embedding module based on a 13B parameter scale "large watt" L0 base model is constructed, which optimizes the embedding space through contrast learning technology to enhance the discrimination of semantic representation. The input text is first segmented into subword units and mapped into an initial semantic vector through an embedding layer, then the global dependency relationship is captured using a self-attention mechanism, and finally a multi-dimensional semantic vector is output.

[0011] Preferably, the step (b) specifically comprises: In the syntax layer analysis, part-of-speech tagging and dependency syntax analysis techniques are used to extract the syntactic structure features of the text; in the semantic layer analysis, named entity recognition and relation extraction techniques are used to capture the core semantic information in the text; in the context-related layer analysis, a discourse coherence modeling technique is introduced to analyze long-range dependencies to improve the understanding ability of complex context. The results of each layer of analysis are stored in the form of feature matrix for subsequent processing.

[0012] Preferably, the step (c) specifically comprises: The dynamic evaluation metric set includes three key categories of metrics: robustness testing metrics based on adversarial examples, used to evaluate the model's performance in the face of malicious perturbations; multimodal contextual relevance scoring metrics, used to measure the model's contextual understanding ability when fusing multimodal data such as images and audio; and cross-linguistic transfer understanding metrics, used to detect the model's generalization performance in different language environments. These metrics are combined in a weighted manner to form a comprehensive evaluation score, reflecting the overall performance of the model.

[0013] Preferably, step (d) specifically includes: Based on the quantitative results of the dynamic evaluation index set, a test report is generated. The test report module integrates visualization tools to display model understanding biases in the semantic embedding space in the form of heatmaps, and reveals the model's weaknesses in specific tasks through statistical analysis. Specific optimization strategies are proposed to address the identified deficiencies, including adjusting the weight distribution in the attention mechanism to enhance long-range dependency handling capabilities, and introducing a reinforcement learning reward function to optimize the inference path and improve logical consistency.

[0014] Preferably, the method further includes a security testing module, specifically including: Noise is injected into semantic vectors using a differential privacy mechanism to simulate external interference scenarios, and the balance threshold between model output stability and data security is quantified. The security testing module can detect the risk of sensitive information leakage in the model output and evaluate the model's robustness to interference through semantic embedding space perturbation analysis. This module is designed to improve the model's reliability in practical applications.

[0015] Preferably, the method supports multimodal input, specifically including: A semantic embedding module maps image, audio, and text data to a unified semantic space. For image data, a convolutional neural network is used to extract visual features; for audio data, short-time Fourier transform is used to extract spectral features; and for text data, the aforementioned semantic embedding module is used to generate semantic vectors. These three data types are mapped to the same high-dimensional space through a shared projection layer, facilitating subsequent evaluation of cross-modal understanding capabilities.

[0016] Preferably, the method employs an adaptive sampling strategy during the testing phase, specifically including: The complexity of test cases is dynamically adjusted based on the model's real-time performance, covering layers from basic semantics to higher-order reasoning. Test case complexity is determined by a set of predefined rules, which are updated based on the model's performance at the current level of the task. This strategy ensures the comprehensiveness and relevance of the testing process.

[0017] Therefore, this invention employs the aforementioned multi-level text understanding testing method based on semantic embedding. Through the collaborative work of the semantic embedding module and the multi-level parsing framework, a deep evaluation of the text understanding model is achieved. The introduction of a dynamic evaluation metric set makes the quantification of model performance more accurate, while the security testing module and cross-modal support capabilities further enhance the model's adaptability in complex scenarios. This method provides a systematic solution for model optimization in the field of natural language processing.

[0018] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all of the following advantages at the same time: This invention achieves in-depth evaluation of text understanding models through the collaborative work of a semantic embedding module and a multi-level parsing framework. The introduction of a dynamic evaluation metric set makes the quantification of model performance more precise, while the security testing module and cross-modal support capabilities further enhance the model's adaptability in complex scenarios. This approach provides a systematic solution for model optimization in the field of natural language processing.

[0019] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0020] The accompanying drawings described below are merely some embodiments. Those skilled in the art can obtain other drawings based on these drawings without any creative effort. In the drawings: Figure 1 A flowchart illustrating the multi-level text understanding testing method based on semantic embedding provided in this embodiment of the invention.

[0021] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0022] The invention will now be described in further detail with reference to the accompanying drawings.

[0023] Please see Figure 1 As shown, this invention provides a multi-level text understanding testing method and platform based on semantic embedding. Its core lies in the collaborative work of a semantic embedding module, a multi-level parsing framework, a dynamic evaluation index set, a security testing module, and a test report generation module to achieve in-depth testing and optimization of the text understanding model. The specific implementation of this invention is described below.

[0024] In practical applications, the first step is to construct a semantic embedding module, which serves as the foundation of the entire testing method and is responsible for mapping the input text into multi-dimensional semantic vectors. In this process, a "big watt" L0 pedestal model with 13B parameters is used as the core component. Contrastive learning techniques are employed to optimize the embedding space, thereby enhancing the discriminative power of the semantic representation. Specifically, the input text is first segmented into word units and transformed into initial semantic vectors through an embedding layer. Subsequently, a self-attention mechanism is used to capture global dependencies, ultimately outputting multi-dimensional semantic vectors. These semantic vectors, as input to the subsequent multi-level parsing framework, directly determine the accuracy and comprehensiveness of the parsing results.

[0025] The multi-level parsing framework is the core parsing component of this invention, covering feature extraction at three levels: syntactic, semantic, and contextual layers. In the syntactic layer, part-of-speech tagging and dependency parsing techniques are used to extract the syntactic structure features of the text. For example, when processing the sentence "Xiaoming likes apples," part-of-speech tagging identifies "Xiaoming" as a noun, "likes" as a verb, and "apple" as a noun, while dependency parsing further reveals the relationship between "Xiaoming" as the subject, "likes" as the predicate, and "apple" as the object. In the semantic layer, named entity recognition and relation extraction techniques are used to capture the core semantic information in the text. For example, for the sentence "Beijing is the capital of China," named entity recognition identifies "Beijing" and "China" as geographical entities, while relation extraction clarifies the "capital-country" relationship between them. In the contextual layer, discourse coherence modeling techniques are introduced to analyze long-range dependencies to improve the understanding of complex contexts. For example, when processing a text containing multiple sentences, analyzing the logical relationships and thematic consistency between sentences ensures that the model can accurately understand the overall semantics. The results of each layer of analysis are stored in the form of a feature matrix for subsequent processing.

[0026] The dynamic evaluation metric set is designed to quantify the model's performance in text understanding tasks. This set includes three key categories of metrics: adversarial robustness metrics, multimodal contextual relevance scoring metrics, and cross-linguistic transfer understanding metrics. The adversarial robustness metrics test the model's performance in the face of external interference by constructing maliciously perturbed input data. For example, by adding noise or replacing homophones in the sentence "I like cats," the model is observed to see if it can still correctly understand the original meaning. The multimodal contextual relevance scoring metrics measure the model's ability to understand context when fusing multimodal data such as images and audio. For example, when processing a news report containing both images and text, the model needs to simultaneously understand the image content and the text description and determine their consistency. The cross-linguistic transfer understanding metrics test the model's generalization performance in different language environments, ensuring its cross-linguistic understanding capabilities. For example, after translating the Chinese sentence "Today the weather is very good" into English, the model is tested to see if it can still maintain semantic consistency. These metrics are weighted and combined to form a comprehensive evaluation score, reflecting the model's overall performance.

[0027] The security testing module is designed to improve the reliability of the model in practical applications. It injects noise into semantic vectors using a differential privacy mechanism to simulate external interference scenarios and quantifies the balance between model output stability and data security. For example, when processing sensitive information, random noise is introduced into the semantic embedding space to detect whether the model will leak user privacy. Furthermore, the security testing module can also evaluate the model's robustness to interference through semantic embedding space perturbation analysis. For instance, it checks whether the model can still output reasonable semantic understanding results when input data is maliciously modified.

[0028] The test report generation module generates test reports based on the quantitative results of the dynamic evaluation metric set. This module integrates visualization tools to display model understanding biases in the semantic embedding space as heatmaps and reveals the model's weaknesses in specific tasks through statistical analysis. For example, when processing long texts, heatmaps may show biases in the model's understanding of certain paragraphs, while statistical analysis further indicates that these problems are mainly concentrated in the contextual association layer. For the identified deficiencies, the test report proposes specific optimization strategies. For example, adjusting the weight distribution in the attention mechanism to strengthen long-range dependency handling capabilities, or introducing reinforcement learning reward functions to optimize inference paths and improve logical consistency.

[0029] In scenarios supporting multimodal input, the semantic embedding module maps image, audio, and text data to a unified semantic space. For image data, a convolutional neural network is used to extract visual features; for audio data, short-time Fourier transform is used to extract spectral features; and for text data, the semantic embedding module generates semantic vectors. These three data types are mapped to the same high-dimensional space through a shared projection layer, facilitating subsequent evaluation of cross-modal understanding capabilities. For example, when processing multimedia content containing images, audio, and text, the model can simultaneously understand information from each modality and determine their consistency.

[0030] During the testing phase, an adaptive sampling strategy is employed to ensure the comprehensiveness and relevance of the testing process. The complexity of test cases is dynamically adjusted based on the model's real-time performance, covering levels from basic semantics to higher-order reasoning. Test case complexity is determined by a set of predefined rules, which are updated based on the model's performance in the current level of task. For example, when the model performs well in basic semantic understanding tasks, the system automatically increases the proportion of higher-order reasoning tasks to further challenge the model's capabilities.

[0031] Through the collaborative work of the semantic embedding module and the multi-level parsing framework, a deep evaluation of the text understanding model was achieved. The introduction of a dynamic evaluation metric set makes the quantification of model performance more accurate, while the security testing module and cross-modal support capabilities further enhance the model's adaptability in complex scenarios. This approach provides a systematic solution for model optimization in the field of natural language processing.

[0032] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0033] In practical applications, the input text is first transformed into a multi-dimensional semantic vector through a semantic embedding module. Specifically, when given a news report, this module segments the text into word units and generates an initial semantic vector through an embedding layer. Subsequently, a self-attention mechanism is used to capture global dependencies in the text, ultimately outputting a high-dimensional semantic vector. These vectors effectively represent the semantic information of the text, providing foundational data support for the subsequent multi-level parsing framework. For example, when processing the sentence "Scientists have discovered a new treatment method," the semantic embedding module can map it into a set of high-dimensional vectors containing syntactic, semantic, and contextual information.

[0034] Next, the multi-level parsing framework performs hierarchical parsing of the semantic vectors. In the syntactic level, the system employs part-of-speech tagging and dependency parsing to extract the syntactic structural features of the text. For example, for the sentence "Scientists have discovered a new treatment method," part-of-speech tagging identifies "scientists" as a noun, "discovery" as a verb, and "method" as a noun, while dependency parsing further reveals that "scientists" is the subject, "discovery" is the predicate, and "method" is the object. In the semantic level, named entity recognition and relation extraction techniques capture the core semantic information of the text. For example, named entity recognition identifies "scientists" as a professional entity and "treatment method" as a medically related entity, while relation extraction clarifies the "research-result" relationship between the two. In the contextual relational level, discourse coherence modeling is introduced to analyze long-range dependencies to improve the understanding of complex contexts. For example, when processing a news report containing multiple sentences, analyzing the logical relationships and thematic consistency between sentences ensures that the model can accurately understand the overall semantics. The results of each level of parsing are stored in the form of a feature matrix for subsequent dynamic evaluation of the metric set.

[0035] The dynamic evaluation metric set is designed to quantify the model's performance in text understanding tasks. In this stage, the robustness test metric based on adversarial examples detects the model's performance in the face of external interference by constructing maliciously perturbed input data. For example, by adding noise or replacing homophones in the sentence "I like cats," the model is observed to see if it can still correctly understand the original meaning. The multimodal contextual relevance scoring metric measures the model's contextual understanding ability when fusing multimodal data such as images and audio. For example, when processing a news report containing images and text, the model needs to simultaneously understand the image content and the text description and determine their consistency. The cross-language transfer understanding metric ensures the model's cross-language understanding ability by detecting its generalization performance in different language environments. For example, after translating the Chinese sentence "Today the weather is very good" into English, the model is tested to see if it can still maintain semantic consistency. These metrics are combined in a weighted manner to form a comprehensive evaluation score, reflecting the model's overall performance.

[0036] The security testing module injects noise into semantic vectors using a differential privacy mechanism to simulate external interference scenarios and quantifies the balance threshold between model output stability and data security. For example, when processing sensitive information, it detects whether the model will leak user privacy by introducing random noise into the semantic embedding space. Furthermore, the security testing module can also evaluate the model's robustness to interference through semantic embedding space perturbation analysis. For example, it detects whether the model can still output reasonable semantic understanding results when input data is maliciously modified.

[0037] The test report generation module generates test reports based on the quantitative results of the dynamic evaluation metric set. This module integrates visualization tools to display model understanding biases in the semantic embedding space as heatmaps and reveals the model's weaknesses in specific tasks through statistical analysis. For example, when processing long texts, heatmaps may show biases in the model's understanding of certain paragraphs, while statistical analysis further indicates that these problems are mainly concentrated in the contextual association layer. For the identified deficiencies, the test report proposes specific optimization strategies. For example, adjusting the weight distribution in the attention mechanism to strengthen long-range dependency handling capabilities, or introducing reinforcement learning reward functions to optimize inference paths and improve logical consistency.

[0038] In scenarios supporting multimodal input, the semantic embedding module maps image, audio, and text data to a unified semantic space. For image data, a convolutional neural network is used to extract visual features; for audio data, short-time Fourier transform is used to extract spectral features; and for text data, the semantic embedding module generates semantic vectors. These three data types are mapped to the same high-dimensional space through a shared projection layer, facilitating subsequent evaluation of cross-modal understanding capabilities. For example, when processing multimedia content containing images, audio, and text, the model can simultaneously understand information from each modality and determine their consistency.

[0039] During the testing phase, an adaptive sampling strategy is employed to ensure the comprehensiveness and relevance of the testing process. The complexity of test cases is dynamically adjusted based on the model's real-time performance, covering levels from basic semantics to higher-order reasoning. Test case complexity is determined by a set of predefined rules, which are updated based on the model's performance in the current level of task. For example, when the model performs well in basic semantic understanding tasks, the system automatically increases the proportion of higher-order reasoning tasks to further challenge the model's capabilities.

[0040] Through the collaborative efforts of the above steps, a deep evaluation of the text understanding model was achieved. The combination of the semantic embedding module and the multi-level parsing framework provides comprehensive semantic feature extraction capabilities, the introduction of a dynamic evaluation metric set makes the quantification of model performance more accurate, and the security testing module and cross-modal support capabilities further enhance the model's adaptability in complex scenarios. This approach provides a systematic solution for model optimization in the field of natural language processing.

[0041] This invention is not limited to the embodiments described above. Anyone should understand that structural changes made under the guidance of this invention, and any technical solutions that are the same as or similar to this invention, fall within the protection scope of this invention. Technical aspects, shapes, and structures not described in detail in this invention are all publicly known technologies.

Claims

1. A multi-level text understanding testing method and platform based on semantic embedding, characterized in that, Includes the following steps: Step (a): Construct a semantic embedding module based on a pre-trained large model to map the input text into a multi-dimensional semantic vector; Step (b) Perform multi-level semantic parsing on the text, covering feature extraction at the syntactic, semantic, and contextual layers; Step (c): Design a dynamic evaluation index set, combining semantic vectors and multi-level features, to quantify the model's accuracy, logical consistency, and contextual relevance in text understanding tasks; Step (d): Generate a test report, provide feedback on potential defects in the model, and propose optimization strategies.

2. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, Step (a) specifically includes: A semantic embedding module based on a "big watt" L0 base model with 13B parameters is constructed. This module optimizes the embedding space through contrastive learning techniques to enhance the discriminativeness of semantic representations. The input text is first segmented into sub-word units and mapped to initial semantic vectors through the embedding layer. Then, a self-attention mechanism is used to capture global dependencies, and finally, a multi-dimensional semantic vector is output.

3. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, Step (b) specifically includes: In the syntactic layer parsing, part-of-speech tagging and dependency parsing techniques are used to extract the syntactic structural features of the text; in the semantic layer parsing, named entity recognition and relation extraction techniques are used to capture the core semantic information in the text; in the contextual layer parsing, discourse coherence modeling techniques are introduced to analyze long-range dependencies to improve the ability to understand complex contexts; the results of each layer of parsing are stored in the form of a feature matrix for subsequent processing.

4. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, Step (c) specifically includes: The dynamic evaluation metric set includes three key categories of metrics: robustness test metrics based on adversarial examples, used to evaluate the model's performance in the face of malicious perturbations; multimodal contextual relevance scoring metrics, used to measure the model's contextual understanding ability when fusing multimodal data such as images and audio; and cross-language transfer understanding metrics, used to detect the model's generalization performance in different language environments. These metrics are combined in a weighted manner to form a comprehensive evaluation score.

5. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, Step (d) specifically includes: Test reports are generated based on the quantitative results of the dynamic evaluation index set. The test report module integrates visualization tools to display model understanding biases in the semantic embedding space in the form of heatmaps, and reveals the model's weaknesses in specific tasks through statistical analysis. Specific optimization strategies are proposed for the discovered defects, including adjusting the weight distribution in the attention mechanism to enhance long-range dependency processing capabilities, and introducing reinforcement learning reward functions to optimize inference paths to improve logical consistency.

6. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, Includes a security testing module, specifically including: The semantic vectors are injected with noise through differential privacy mechanism to simulate external interference scenarios and quantify the balance threshold between model output stability and data security. The security testing module can detect the risk of leakage of sensitive information in the model output and evaluate the model's anti-interference ability through semantic embedding space perturbation analysis.

7. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, Supports multimodal input, specifically including: The semantic embedding module maps image, audio, and text data to a joint semantic space. For image data, a convolutional neural network is used to extract visual features. For audio data, short-time Fourier transform is used to extract spectral features. For text data, the aforementioned semantic embedding module is used to generate semantic vectors. The three data types are mapped to the same high-dimensional space through a shared projection layer, which facilitates subsequent cross-modal understanding ability assessment.

8. The multi-level text understanding testing method and platform based on semantic embedding according to claim 1, characterized in that, An adaptive sampling strategy is adopted during the testing phase, specifically including: The complexity of test cases is dynamically adjusted based on the real-time performance of the model, covering layers from basic semantics to higher-order reasoning. The complexity of test cases is determined by a set of predefined rules, which are updated based on the model's performance in the current level of task.

9. A multi-level text understanding testing method and platform based on semantic embedding according to claim 2, characterized in that, The semantic embedding module optimizes the embedding space through contrastive learning techniques, specifically including: A contrastive loss function is introduced into the embedding space to enhance the discriminative power of semantic representations by constraining the semantic distance between positive and negative samples.

10. The multi-level text understanding testing method and platform based on semantic embedding according to claim 4, characterized in that, The dynamic evaluation metrics set specifically includes robustness test metrics based on adversarial examples: By constructing maliciously perturbed input data, the accuracy of the model's semantic understanding in the face of external interference is tested; the perturbation methods include character replacement, insertion or deletion operations, and the perturbation range is limited to the range of preset rules.

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