Constituency Parsing for Valid Dependency Structures in Casual Text
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Solution Overview
Problem
Existing natural language parsing techniques often produce invalid dependency parses across clause boundaries, particularly in informal texts lacking proper punctuation and spelling, due to reliance on noun chunking.
Innovation Solution
A computing device is designed to load natural language text data, apply a constituency parser to generate a parse tree, and then use a constituency rule to identify non-overlapping clause boundaries, allowing for separate dependency parsing of each clause to avoid invalid parses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical dependency parsing techniques are used, then parsing speed is maintained, but parsing accuracy deteriorates due to invalid dependency parses across clause boundaries
Solution Approach 1:
The patent divides the text into multiple clauses using constituency parsing and clause boundary detection before performing dependency parsing. Each clause is processed independently, preventing invalid dependencies across clause boundaries. This segmentation approach directly resolves the technical contradiction by improving parsing accuracy through clause-level isolation while managing complexity through systematic text division.
2Reliability
If noun chunking is used as an initial parsing step, then parsing efficiency is maintained, but parsing reliability deteriorates in casual text lacking proper punctuation
Solution Approach 1:
The patent performs constituency parsing and clause boundary detection as preliminary actions before dependency parsing. This preliminary segmentation based on syntactic structure rather than noun chunking improves reliability for casual text while maintaining efficiency through automated rule-based clause identification. The constituency parse tree provides a robust foundation that works well with informal text structures.
3Measurement precision
If dependency parsing is applied to the entire text at once, then processing simplicity is maintained, but parsing precision deteriorates due to incorrect dependencies between independent clauses
Solution Approach 1:
The patent segments text into clauses using constituency grammar rules and applies dependency parsing separately to each clause. This ensures that dependencies are only formed within clause boundaries, eliminating invalid cross-clause dependencies. The segmentation is achieved through automated clause boundary detection based on syntactic structure, balancing precision requirements with architectural feasibility.
Data Source
AI summary
Technologies for natural language processing include a computing device that loads natural language text data that includes multiple words, such as a naturally elicited response or comment data. The computing device applies a constituency parser to the natural language text data to generate a constituency parse tree. The constituency parse tree may include multiple nodes that each correspond to a hierarchical constituent of the natural language text. The computing device applies a constituency rule to identify clauses in the natural language text data. The constituency rule may identify a left-most daughter of each subordinate clause node of the constituency parse tree. Each subordinate clause is distinct, preventing dependencies from crossing clause boundaries. The computing device applies a dependency parser to each clause to generate a corresponding dependency parse. Each dependency parse may include a graph with nodes corresponding to words of the clause and edges corresponding to dependency relationships.