Chatbot Semantic Analysis Using Source Code Conversion
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Solution Overview
Problem
Chatbots struggle to provide accurate answers when technical terms from source code are used in questions, as they lack the necessary semantics stored in their databases, leading to inadequate understanding and response generation.
Innovation Solution
Implementing a text semantic analysis method that performs source code analysis in conjunction with natural language processing to convert text keywords into source code information, enabling deeper semantic analysis and improving answer accuracy for technical queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a chatbot uses only dictionary semantic analysis for text processing, then the system complexity remains low, but the chatbot cannot accurately understand technical terms from source code
Solution Approach 1:
The system segments the semantic analysis process into two distinct parts: dictionary-based semantic analysis for general terms and source code-based semantic analysis for technical terms. This segmentation allows the chatbot to handle different types of terms through specialized analysis paths, improving overall accuracy without requiring a complete redesign of the system.
Solution Approach 2:
The system introduces an intermediary component that detects whether a term is a technical term and routes it to the appropriate analysis method. This intermediary layer manages the complexity by providing a clear interface between the simple dictionary analysis and the more complex source code analysis, allowing the system to maintain low complexity while achieving high precision.
2Reliability
If a chatbot analyzes only common dictionary terms, then the processing speed is fast, but it fails to provide accurate answers for technical questions containing source code terminology
Solution Approach 1:
The system applies partial action by performing source code analysis only on terms that are identified as technical terms, rather than analyzing every term in the text. This selective approach ensures that the chatbot maintains fast processing speed for common terms while investing additional time only when necessary for technical accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing the text to identify and flag technical terms before the main semantic analysis. This preliminary identification allows the system to prepare the appropriate analysis method in advance, reducing the overall processing time when technical terms are encountered.
3Loss of information
If a chatbot database stores only general semantic information, then the database size remains small, but it cannot provide accurate semantics for source code-related technical terms
Solution Approach 1:
The system implements multi-functionality by using the source code information stored in the database for multiple purposes: it serves as both the knowledge base for semantic analysis and the reference for identifying technical terms. This universal use of source code information allows the system to maintain a compact database while achieving complete semantic coverage for both general and technical terms.
Data Source
AI summary
A text semantic analysis method using source code analysis includes receiving, by the server, input data including a text from the terminal, generating, by the server, a text analysis result including semantics of the text by performing natural language processing on the text based on the input data; and generating, by the server, output data corresponding to the semantics of the text based on the text analysis result and providing the output data to the terminal. The generating of the text analysis result includes modifying, by the server, the text by converting a text keyword into source code information corresponding to the text keyword when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the server, and generating, by the server, the text analysis result by performing analysis on the modified text.