graph LR
THULAC_Public_API["THULAC Public API"]
Text_Preprocessing_Module["Text Preprocessing Module"]
Core_Lexical_Analysis_Engine["Core Lexical Analysis Engine"]
Feature_Generation_Module["Feature Generation Module"]
Model_Data_Management["Model & Data Management"]
Post_processing_Output_Formatting["Post-processing & Output Formatting"]
THULAC_Public_API -- "Sends Raw Text To" --> Text_Preprocessing_Module
THULAC_Public_API -- "Initiates Model Loading In" --> Model_Data_Management
THULAC_Public_API -- "Receives Formatted Output From" --> Post_processing_Output_Formatting
Text_Preprocessing_Module -- "Forwards Cleaned Text To" --> Core_Lexical_Analysis_Engine
Core_Lexical_Analysis_Engine -- "Requests Features From" --> Feature_Generation_Module
Core_Lexical_Analysis_Engine -- "Receives Linguistic Data From" --> Model_Data_Management
Core_Lexical_Analysis_Engine -- "Sends Raw Results To" --> Post_processing_Output_Formatting
Feature_Generation_Module -- "Returns N-gram Features To" --> Core_Lexical_Analysis_Engine
Model_Data_Management -- "Provides Linguistic Models To" --> Core_Lexical_Analysis_Engine
Post_processing_Output_Formatting -- "Returns Formatted Text To" --> THULAC_Public_API
click THULAC_Public_API href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/THULAC-Python/THULAC_Public_API.md" "Details"
click Text_Preprocessing_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/THULAC-Python/Text_Preprocessing_Module.md" "Details"
click Core_Lexical_Analysis_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/THULAC-Python/Core_Lexical_Analysis_Engine.md" "Details"
click Feature_Generation_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/THULAC-Python/Feature_Generation_Module.md" "Details"
click Model_Data_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/THULAC-Python/Model_Data_Management.md" "Details"
click Post_processing_Output_Formatting href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/THULAC-Python/Post_processing_Output_Formatting.md" "Details"
The THULAC-Python project implements a clear, pipeline-driven architecture for Chinese lexical analysis. At its core, the THULAC Public API serves as the user's gateway, orchestrating the flow of text through a series of specialized modules. Raw input first undergoes normalization and cleaning by the Text Preprocessing Module. The prepared text then enters the Core Lexical Analysis Engine, which performs the fundamental segmentation and part-of-speech tagging. This engine's accuracy is underpinned by linguistic models and data structures managed by the Model & Data Management component, and it dynamically leverages features generated by the Feature Generation Module. Post-analysis, the raw results are refined and formatted by the Post-processing & Output Formatting module before being presented back to the user. This modular design facilitates maintainability, allows for performance optimizations, and provides a clear, sequential data flow, making it ideal for visual representation as a directed graph.
THULAC Public API [Expand]
The primary user-facing interface and orchestrator of the entire text processing pipeline. It handles input/output, exposes core functionalities, and manages the overall flow.
Related Classes/Methods:
Text Preprocessing Module [Expand]
Responsible for initial cleaning, normalization, and transformation of raw input text, including character encoding handling and traditional-to-simplified Chinese conversion.
Related Classes/Methods:
Core Lexical Analysis Engine [Expand]
The central processing unit for character-based segmentation and part-of-speech tagging, applying dynamic programming algorithms and utilizing linguistic models.
Related Classes/Methods:
Feature Generation Module [Expand]
Generates linguistic features (e.g., N-grams) from input characters, which are crucial inputs for the Core Lexical Analysis Engine's tagging decisions.
Related Classes/Methods:
Model & Data Management [Expand]
Manages the loading, initialization, and efficient access of pre-trained linguistic models and underlying data structures (e.g., Double Array Tries, character-based models).
Related Classes/Methods:
Post-processing & Output Formatting [Expand]
Refines the raw output from the Core Lexical Analysis Engine, applying post-tagging rules, filtering, and formatting the final results for user consumption.
Related Classes/Methods: