Cross-language thinking training system based on semantic units

Abstract

The invention discloses a thinking training system and method based on semantic unit cross-language conversion, and belongs to the field of intelligent education technology and natural language processing cross technology. The system includes a semantic chunk divider, a conversion processor, and a cognitive adapter. The semantic chunk divider adopts a dual verification mechanism of dependency syntactic analysis and semantic role labeling, and a predicate verb is taken as a core to divide a complete semantic unit; the conversion processor constructs a semantic intermediate representation based on a graph structure, and maintains a logic relationship between semantic units in cross-language conversion; and the cognitive adapter monitors the cognitive load of the user in real time and dynamically adjusts the complexity of the semantic unit. Through triple technical means of semantic unit division, graph structure conversion and cognitive dynamic adaptation, visualization of the cross-language thinking conversion process and personalized adaptation of training content are achieved, and the black box problem and the language training rigidity problem of an existing machine translation system are effectively solved.

Description

I. Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of smart education technology and natural language processing, specifically involving a cross-language thinking training system and its implementation method based on semantic unit processing. II. Background Technology

[0002] The following technical problems currently exist in the fields of language learning and machine translation:

[0003] Process black box: End-to-end translation models cannot show the semantic unit transformation process. Training discretization: Syntax analysis, vocabulary memorization, and expression training are isolated from each other.

[0004] Adaptation rigidity: lack of dynamic adjustment mechanism based on cognitive state III. Summary of the Invention

[0005] (I) Purpose of the Invention

[0006] The present invention aims to provide a cross-language thinking training system and method that can visualize the language conversion process and support adaptive training.

[0007] (II) Technical Solution

[0008] 1. System Architecture

[0009] This system comprises five core modules:

[0010] (1) Semantic parsing module

[0011] -Language Feature Extractor: Extracts syntactic and semantic features based on a pre-trained model.

[0012] - Semantic block divider: Divides complete semantic units through syntactic boundary analysis.

[0013] -Structure labeler: Adds multidimensional tags to semantic units

[0014] (2) Conversion Processing Module

[0015] - Intermediate representation layer: Constructing a language-independent semantic framework

[0016] - Lexical Mapper: Achieving cross-language mapping based on attention mechanisms

[0017] - Sequence maintainer: Maintains the logical relationships between semantic units

[0018] (3) Sequence Reconstruction Module

[0019] - Multilingual rule base: stores word order rules and expression habits

[0020] -Structure Adapter: Sequence Recombination Based on Target Language Rules

[0021] - Smoothness Optimizer: Performs naturalness verification and optimization.

[0022] (4) Output generation module

[0023] - Context analyzer: analyzes usage scenarios and user identities

[0024] - Expression optimizer: Ensures compliance with pragmatic norms

[0025] - Multimodal output: Generates text, speech, and other forms of output.

[0026] (5) Control Module

[0027] - Process scheduler: Manages execution order and data flow.

[0028] - Quality evaluator: Monitors processing quality and adjusts parameters.

[0029] - User adapter: Adjust processing strategies based on user behavior.

[0030] 2. Work Process

[0031] (1) Input analysis and semantic parsing

[0032] -Text preprocessing: word segmentation, part-of-speech tagging, named entity recognition -Syntactic analysis: dependency parsing and constituent parsing

[0033] - Semantic segmentation: Dividing a sentence into complete semantic units

[0034] -Structural annotation: Add syntactic and semantic information tags

[0035] (2) Language conversion that preserves structure

[0036] - Establishing intermediate representations: Constructing a language-independent semantic framework

[0037] - Lexical alignment transformation: Mapping based on attention mechanism

[0038] - Sequence relationship preservation: Maintaining logical relationships

[0039] (3) Sequence reconstruction of target word order

[0040] - Rule matching: Retrieve rules in the target language

[0041] - Sequence optimization: Adjusting the order of semantic units

[0042] - Fluency check: Ensures that the language conforms to common usage.

[0043] (4) Context-adaptive output generation

[0044] -Contextual integration: Optimize expression based on the context.

[0045] - Pragmatic adjustment: conforming to social and cultural norms

[0046] - Multimodal generation: Output based on device capabilities

[0047] (III) Technical Effects

[0048] Process Visualization: Fully presents the language conversion path, showcasing differences in semantic structure and thought logic. Personalized Training: Dynamically adjusts training content and pace based on user performance and cognitive state. Quantifiable Results: Tracks learning outcomes through a multi-dimensional evaluation system, providing a basis for optimization. IV. Description of the attached drawings

[0049] Figure 1 System overall architecture diagram

[0050] Display the connections, data flow, and interface specifications among the five core modules: semantic parsing module, transformation processing module, sequence reconstruction module, output generation module, and control module.

[0051] Figure 2 Four-stage processing flow diagram

[0052] It demonstrates the execution order, data transformation process, and input / output specifications of the four stages: input analysis, language conversion, sequence reconstruction, and output generation.

[0053] Figure 3 Internal structure diagram of the semantic parsing module

[0054] Explain the composition, collaborative relationship, and data processing mechanism of the language feature extractor, semantic block divider, and structure annotator within the semantic parsing module.

[0055] Figure 4 Multi-device collaborative architecture diagram

[0056] The system displays a distributed deployment solution for various devices such as smart terminals and wearable devices, including device discovery, task allocation, and data synchronization processes.

[0057] Figure 5 User interface layout diagram

[0058] The system demonstrates the interface partitioning design in practical applications, including the layout structure of the language input area, process visualization area, user control area, and multimodal output area.

[0059] Figure 6 Training and evaluation system architecture diagram

[0060] Explain the architecture design of the learning progress tracking, ability assessment model, and personalized recommendation engine, and demonstrate the assessment indicator collection and feedback mechanism. V. Detailed Implementation Methods

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection.

[0062] Example 1: Basic Language Conversion Training

[0063] Scenario: Users are practicing English-to-Chinese translation, focusing on understanding the differences in expression logic and structure between English and Chinese.

[0064] Input: English sentence

[0065] "The researcher who made this discovery was awarded the Nobel Prize."

[0066] Processing procedure:

[0067] 1. Semantic parsing stage

[0068] - Input preprocessing: word segmentation and part-of-speech tagging

[0069] -Grammatical analysis: Identifying the structure of main clauses and relative clauses

[0070] - Semantic segmentation results:

[0071] [The researcher][who made this discovery][was awarded][the NobelPrize]

[0072] -Structural annotations:

[0073] -"The researcher": subject, noun phrase

[0074] -"who made this discovery": a relative clause modifying the main idea.

[0075] -"was awarded": predicate, verb-object structure

[0076] - "the Nobel Prize": object, noun phrase

[0077] 2. Conversion Processing Stage

[0078] - Intermediate representation: Constructing a language-independent semantic framework

[0079] - Vocabulary mapping results:

[0080] This researcher was awarded the Nobel Prize for this discovery.

[0081] - Sequence preservation: Maintaining the original order of semantic units

[0082] 3. Sequence Reconstruction Stage

[0083] - Rule matching: Chinese prefers pre-modifiers

[0084] -Sequence adjustment:

[0085] The researcher who made this discovery was awarded the Nobel Prize.

[0086] - Smoothness optimization: Adjusted expression transitions

[0087] 4. Output Generation Stage

[0088] - Context adaptation: Formal academic setting

[0089] - Pragmatic optimization:

[0090] The researchers who made this discovery were awarded the Nobel Prize.

[0091] - Output: Text display combined with voice broadcast technology. Effect: Users can clearly observe the conversion process from English post-modifiers to Chinese pre-modifiers and understand the differences in thinking between the two languages ​​regarding the position of modifiers.

[0092] Example 2: Adaptive Training Process

[0093] System components:

[0094] -User modeling unit: Records language proficiency, learning progress, and cognitive status.

[0095] -Training Planning Unit: Developing Personalized Training Plans

[0096] - Progress Tracking Unit: Monitors user performance and updates the model.

[0097] Workflow:

[0098] 1. Training Mode Selection

[0099] - Language proficiency < 0.3: Detailed analysis mode

[0100] - Language level 0.3-0.7: Guided practice mode

[0101] - Language proficiency > 0.7: Free generation mode 2. Training content adjustment

[0102] -For beginners: Complete process + detailed explanation + basic tasks

[0103] -Advanced Level: Key Processing Steps + Moderate Hints + Medium-Level Tasks

[0104] - Advanced level: Hidden processing details + minimal feedback + challenging task 3, model update mechanism

[0105] - Update language proficiency scores based on training performance

[0106] - Dynamically adjust training plans and content difficulty

[0107] Example 3: Multi-device collaborative implementation

[0108] Equipment configuration:

[0109] - Smart glasses: Display augmented reality content - Smart headphones: Provide audio interaction - Smartphones: Core computing processing - Smart bracelets: Monitor physiological signals

[0110] Collaborative process:

[0111] 1. Device discovery and connection

[0112] -Automatic detection of available devices

[0113] - Establish a secure communication connection

[0114] - Negotiation Function Allocation Scheme

[0115] 2. Collaborative task execution

[0116] - Glasses: Augmented Reality Visual Cue

[0117] - Headphones: Voice guidance and interactive audio

[0118] -Mobile Phones: Semantic Analysis and Language Transformation

[0119] -Wristband: Monitors attention levels

[0120] 3. Data synchronization and integration

[0121] - Collect user data from various devices

[0122] -Unified processing and analysis

[0123] - Generate training reports and optimization suggestions

[0124] Technical features:

[0125] 1. Process Visualization: Displays the processing in stages, supporting interactive viewing of intermediate results.

[0126] 2. Adaptive training: Dynamically adjusts difficulty and content based on user skill level.

[0127] 3. Multi-device collaboration: Automatic device discovery, dynamic task allocation, and real-time data synchronization. 4. Error handling: Multi-level error detection and categorized correction strategies.

[0128] Industrial Applications:

[0129] This invention has promising applications in the following fields:

[0130] - Smart Education Sector: Language Training Institution Platforms, School Education Support Systems, Self-Study Smart Assistants - Smart Hardware Sector: Smart Glasses / Headphones for Learning Applications

[0131] -Technical services areas: online education platforms, cross-border communication tools, and language assessment systems.

Claims

1. A thinking training system based on cross-language conversion of semantic units, characterized in that, include: The semantic block divider employs a dual verification mechanism of dependency parsing and semantic role labeling, dividing complete semantic units with the predicate verb as the core. The translation processor constructs a semantic intermediate representation based on a graph structure to maintain the logical relationships between semantic units during cross-language translation; the cognitive adapter monitors the user's cognitive load in real time and dynamically adjusts the complexity of semantic units.

2. The system according to claim 1, characterized in that, The semantic block divider ensures that each semantic unit is both a grammatically complete subtree and an independent meaning carrier.

3. The system according to claim 1, characterized in that, The transformation processor visualizes the attention weights to show the correspondence between semantic units of the source language and the target language.

4. A thinking training method based on cross-language conversion of semantic units, characterized in that, include: Complete semantic units are divided using a dual verification mechanism; Construct a semantic intermediate representation of the graph structure and maintain logical relationships; The semantic unit complexity is dynamically adjusted based on real-time cognitive load.

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