Dependency Parser NDCG Optimization via Tree Edit Distance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search engines struggle to effectively improve long query results, which are often worse than short query results, due to limitations in analyzing complex relationships among terms.
Innovation Solution
A supervised technique is employed to train a dependency parser that optimizes Normalized Discounted Cumulative Gain (NDCG) by incorporating a weighted tree edit distance between query and document parse trees into the ranking function, using parser parameters to enhance the ranking algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional crawlers analyze documents as flat text files examining words and their positions, then the analysis process is simple and fast, but the ability to understand complex relationships among terms deteriorates
Solution Approach 1:
The patent segments the flat text into a hierarchical parse tree structure using syntactic parsing. This divides the document into nested phrases and clauses with grammatical relationships, allowing the system to maintain processing efficiency while capturing complex term relationships through the tree hierarchy rather than treating text as a flat sequence
Solution Approach 2:
The patent transforms the one-dimensional flat text structure into a two-dimensional hierarchical parse tree with multiple levels of grammatical organization. This dimensional transformation enables the system to capture long-range dependencies and complex relationships among terms by traversing the tree structure, while still maintaining computational tractability through structured representation
2Manufacturing precision
If parser parameters are optimized for syntactic accuracy, then the parse tree structure is precise, but the ranking performance for search results deteriorates
Solution Approach 1:
The patent changes the optimization parameters from purely syntactic accuracy metrics to ranking performance metrics. By retraining the parser to optimize for search result ranking rather than just grammatical correctness, the system achieves parse trees that are specifically tailored for information retrieval tasks, improving the relevance of search results while maintaining sufficient syntactic structure
Solution Approach 2:
The patent creates a simplified or adapted version of the parse tree structure that prioritizes features useful for ranking over complete syntactic accuracy. This copied or modified tree structure retains enough grammatical information to capture term relationships while being optimized for the specific purpose of ranking search results rather than general linguistic analysis
Data Source
AI summary
A supervised technique uses relevance judgments to train a dependency parser such that it approximately optimizes Normalized Discounted Cumulative Gain (NDCG) in information retrieval. A weighted tree edit distance between the parse tree for a query and the parse tree for a document is added to a ranking function, where the edit distance weights are parameters from the parser. Using parser parameters in the ranking function enables approximate optimization of the parser's parameters for NDCG by adding some constraints to the objective function.