Syntactic Classification for Diverse NLP Training Sets
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Solution Overview
Problem
Deep question answering systems face challenges in achieving grammatical diversity in their training sets, leading to overfitting and poor predictive performance due to syntactic similarity among questions, which hinders their ability to answer a variety of question types effectively.
Innovation Solution
A method for syntactically classifying natural language sentences by generating a full syntactic parse tree and traversing it to record traversal paths, allowing for the identification and recording of syntactic information, which is used to create a diverse training set by ensuring varied grammatical structures and semantic relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional sentence classification methods are used, then classification speed is improved, but classification precision deteriorates due to reliance on bag of words features rather than full syntactic parse
Solution Approach 1:
The patent segments the syntactic parse tree into specific paths starting from terminal nodes representing elements of interest and traversing upward through intermediate nodes to the root node. This segmentation allows the system to extract only the relevant syntactic information needed for classification, rather than processing the entire parse tree, thus maintaining precision while improving efficiency.
2Reliability
If diverse training sets are created to prevent overfitting, then predictive performance is improved, but system complexity increases due to the need for syntactic analysis and classification
Solution Approach 1:
The patent extracts specific syntactic information by traversing paths from terminal nodes representing elements of interest upward through the parse tree. This extraction focuses only on the relevant syntactic relationships needed for diversity measurement, avoiding the need to analyze and store complete parse trees, thus reducing system complexity while maintaining the ability to create diverse training sets.
3Loss of information
If full syntactic parse trees are generated for all sentences, then syntactic information completeness is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary syntactic parsing to generate parse trees, but then immediately traverses them to extract only the essential syntactic paths needed for classification. This preliminary extraction of critical information allows the system to maintain syntactic completeness where needed while avoiding unnecessary processing of redundant parse tree details, thus reducing overall processing time.
Data Source
AI summary
A grammatically diverse test set of natural language sentences for a deep question answering system is provided by analyzing a given sentence to characterize its syntactical classification, and adding the sentence to the test set if its classification is sufficiently different from other sentences already in the test set. A particular sentence may be selected for inclusion according to a desired syntactic distribution. Multiple sentences having the exact same classification may be allowed subject to a maximum number of such sentences. The test set is adapted to an element of interest by characterizing each syntactical classification relative to the element of interest. The analysis derives a parse tree, identifies a particular node of the tree corresponding to the element of interest, and extracts syntactic information by traversing the tree starting at the particular node and ending at the root node of the tree according to different traversal schemes.