Stop Word Detection via Dependency Tree Root Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional question answering (QA) systems face challenges in accurately identifying irrelevant or harmful keywords, which can reduce the system's accuracy in providing relevant answers.
Innovation Solution
The method involves generating dependency trees for questions and answers, identifying words near the root nodes of questions, and determining if these words are present in a threshold number of associated answers. Words that do not meet this criterion are identified as stop words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of stop words is used, then accuracy of stop word selection is improved, but productivity and time consumption are worsened
Solution Approach 1:
The system performs self-service by automatically identifying stop words through dependency tree analysis without requiring manual intervention. The computer generates dependency trees, identifies root nodes, and determines stop words based on presence in answer sentences, enabling the system to autonomously improve its own performance while maintaining high accuracy.
Solution Approach 2:
The manual mechanical process of stop word identification is replaced with an automated computational system. The computer uses natural language processing and dependency tree algorithms to substitute human analysts, dramatically reducing time consumption while maintaining or improving identification accuracy through consistent application of linguistic rules.
2Measurement precision
If domain-specific stop words are filtered out, then measurement precision is improved, but adaptability to different domains is worsened
Solution Approach 1:
The stop word list is made dynamic and adaptable to different domains. Instead of using a static, fixed list of stop words, the system generates domain-specific stop words by analyzing dependency trees of questions and answers within each domain. This allows the stop word identification to adapt dynamically to different domains while maintaining high measurement precision for each specific domain.
Solution Approach 2:
The system applies local quality by tailoring stop word identification to specific domains rather than using a universal approach. Each domain receives customized stop words based on its unique linguistic patterns and characteristics, as determined by analyzing dependency trees of domain-specific question-answer pairs, thereby improving accuracy for each local domain context.
3Productivity
If root node words are automatically identified, then productivity is improved, but measurement precision may be worsened due to potential false positives
Solution Approach 1:
The system uses feedback by comparing the frequency and distribution of root node words across multiple answer sentences. Root nodes that appear frequently in questions but not in corresponding answers are identified as stop words, while those that do appear in answers are retained. This feedback mechanism allows automatic identification while maintaining high precision by validating candidate stop words against actual answer data.
Solution Approach 2:
The system performs preliminary action by generating dependency trees and identifying candidate root nodes before final stop word determination. This preliminary analysis allows the system to pre-identify potential stop words based on syntactic structure, then refine the list through comparison with answer sentences, ensuring both productivity and precision in the final stop word identification.
Data Source
AI summary
Dependency trees are generated for questions and answers of a question answering (QA) corpus in which the answers are associated with the questions. Generating the dependency trees includes identifying root nodes. A word near an identified root node of one of the questions is compared to words of answers associated with the one of the questions. The word is determined to be in less than a threshold number of the associated answers. The word is identified as a stop word.