Question Generation via Semantic Syntactic Tree Node Mapping
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Solution Overview
Problem
Existing question generation systems in linguistics fail to fully leverage semantic and syntactic aspects of language, resulting in inferior performance in generating questions that can augment or complete reference texts, particularly in applications like medical diagnostics.
Innovation Solution
The system generates questions by forming semantic and syntactic trees from reference texts, identifying common and unique nodes, and mapping them to form question fragments that can augment knowledge gaps, using techniques such as semantic maximal common subtree identification and syntactic generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic and syntactic trees are formed and compared to identify unique nodes, then question generation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the language analysis process into distinct semantic tree construction and syntactic tree construction phases, allowing independent optimization of each component while maintaining overall accuracy. This segmentation enables the complex task of question generation to be broken down into manageable sub-tasks that can be processed separately and then integrated.
Solution Approach 2:
The patent introduces semantic trees and syntactic trees as intermediary structures that mediate between the input text and the final generated questions. These intermediate representations capture both meaning and grammatical structure, allowing the system to systematically identify knowledge gaps without directly processing the raw text and target question in a single step.
2Loss of information
If semantic maximal common subtree identification is used to find unique nodes, then completeness of knowledge gap identification is improved, but computational time increases
Solution Approach 1:
The system performs preliminary construction of semantic trees and syntactic trees before identifying the maximal common subtree and unique nodes. This preliminary action organizes the text information into structured representations in advance, making the subsequent comparison and identification processes more efficient and systematic, rather than searching for knowledge gaps directly from raw text.
Solution Approach 2:
The patent focuses on identifying the maximal common subtree and the specific unique nodes that represent knowledge gaps, rather than performing a complete analysis of all possible semantic and syntactic variations. This partial action approach captures the essential information needed for question generation while avoiding unnecessary computational overhead from exhaustive analysis.
3Manufacturing precision
If mapping between semantic nodes and syntactic nodes is performed, then quality of generated questions is improved, but processing complexity increases
Solution Approach 1:
The mapping process focuses on establishing correspondences between specific semantic nodes and syntactic nodes at local positions in the tree structures, rather than requiring a global transformation of the entire trees. This local quality approach allows the system to precisely identify where knowledge gaps exist in terms of both meaning and grammar, improving question quality without the complexity of comprehensive global mapping.
Solution Approach 2:
The patent employs a unified mapping mechanism that simultaneously handles both semantic and syntactic correspondence between nodes. This multi-functional mapping process identifies knowledge gaps while also ensuring grammatical correctness of the generated questions, achieving multiple objectives through a single integrated operation rather than separate processing steps.
Data Source
AI summary
Disclosed systems relate to generating questions from text. In an example, a method includes forming a first semantic tree from a first reference text and second semantic tree from a second reference text. The method includes identifying a set of semantic nodes that are in the first semantic tree but not in the second semantic tree. The method includes forming a first syntactic tree for the first reference text and a second syntactic tree for the second reference text. The method includes identifying a set of syntactic nodes that are in the first syntactic tree but not in the second syntactic tree. The method includes mapping the set of semantic nodes to the set of syntactic nodes by identifying a correspondence between a semantic node and a syntactic node, forming a question fragment from a normalized word, and providing the question fragment to a user device.