Dependency Parsing for Grammar-Based Sentence Search Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sentence search technologies fail to consider grammatical structure and similarity, leading to inaccurate results in fields like language education and natural language processing, while conventional modification relation analysis and segmentation technologies struggle with complex sentences and user-specific needs.
Innovation Solution
A system utilizing dependency parsing to analyze sentence structure, enabling grammar-based search, modification relation analysis, and customizable segmentation, providing accurate and user-specific results through a computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sentence search technologies compare and search sentences based on words such as similarity of words included in the sentence or part of speech information, then the search process is simple and fast, but the search accuracy is low and cannot consider grammatical meaning or structure
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search mechanisms with a neural network-based semantic analysis system. The neural network model automatically extracts semantic features and grammatical structures from sentences, substituting the simple word-comparison approach with an intelligent system that understands sentence meaning and structure, thereby improving search accuracy without requiring manual feature engineering
Solution Approach 2:
The patent transforms the search parameters from simple word similarity and part-of-speech information to comprehensive semantic features including grammatical structure, sentence meaning, and contextual relationships. By changing the parameter space to include these deeper linguistic features extracted by the neural network, the system achieves more accurate sentence matching while the automated extraction process manages the increased complexity
2Measurement precision
If conventional modification relation analysis technologies display a modification relation centered on a main word from the beginning of a sentence, then the analysis is simple to implement, but modifiers or prepositional phrases in the back may not be accurately analyzed
Solution Approach 1:
The patent inverts the traditional analysis approach by not starting from the main word at the beginning of the sentence, but rather allowing the neural network to identify modification relations throughout the entire sentence structure simultaneously. This inversion of the analysis direction enables accurate capture of back-modifiers and prepositional phrases that were previously missed by forward-centered analysis
Solution Approach 2:
The neural network model is designed to handle multiple types of modification relations universally, including but not limited to modifiers and prepositional phrases at any position in the sentence. The single model performs multiple functions of identifying different grammatical relationships throughout the sentence, replacing the need for separate analysis methods for different modification types
3Adaptability or versatility
If constituency parsing mainly used rule-based approaches, then the method is easy to understand, but it has limitations in processing new sentence structures because it is difficult to comprehensively write rules
Solution Approach 1:
The patent replaces the mechanical rule-based parsing system with a neural network-based semantic analysis system. Instead of relying on manually crafted grammatical rules that cannot adapt to new sentence structures, the neural network learns parsing patterns from data and automatically generalizes to new structures, providing the needed adaptability while the model handles the system complexity internally
Solution Approach 2:
The patent transitions from a rule-based parameter system to a learned parameter system. The neural network automatically learns the parameters and features needed for parsing new sentence structures from training data, eliminating the need for comprehensive manual rule writing. This parameter transformation enables the system to adapt to new grammatical structures without explicit programming
4Productivity
If constituency parsing mainly used statistical approaches, then the method can handle various sentence structures, but it takes too much time to process complex sentence structures
Solution Approach 1:
The patent replaces traditional statistical parsing methods with a neural network-based semantic analysis system that is optimized for faster processing. The neural network architecture, particularly when implemented with efficient computational graphs and hardware acceleration, achieves both high processing speed and high segmentation accuracy by learning direct mappings from input sentences to parsed structures without the computational overhead of statistical methods
Solution Approach 2:
The patent employs a composite approach combining elements of neural network architecture with optimized computational methods. The system integrates multiple processing components including embedding layers, attention mechanisms, and parsing heads into a unified model that achieves both speed and accuracy, similar to how composite materials combine different materials to achieve superior properties
5Adaptability or versatility
If conventional technologies provide uniform segmentation results for an identical sentence, then the system is simple to operate, but it may not meet the user's needs for different segmentation purposes
Solution Approach 1:
The patent introduces dynamic adaptability into the segmentation system by allowing the neural network to adjust its analysis based on user needs and context. The system can dynamically change its segmentation behavior depending on the purpose, providing customized results for different tasks while maintaining a unified underlying architecture. This dynamic capability enables versatility without requiring multiple separate systems
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system includes: a communication interface connected to another computing device and receiving an input related to information about a grammar to be searched; and at least one processor configured to obtain a query including information about the grammar to be searched based on the received input, compare grammar structure information corresponding to the grammar included in the obtained query with sentence structure information of each sentence stored in a database, and obtain a search result including at least one sentence having the grammar structure information according to a comparison result, wherein the grammar structure information and the sentence structure information include structure information based on dependency parsing.