Natural Language Query Parser with Ambiguity Resolution
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Solution Overview
Problem
Conventional natural language parsers often fail to meet the high standards of applications with low tolerance for misinterpretation due to quality gaps, and improving parsing quality through retraining and high-quality training data is resource- and time-consuming.
Innovation Solution
The method involves obtaining a natural language query, parsing it into structured operations for a knowledge base's APIs, detecting ambiguities, and allowing user interaction to resolve them by adding words, symbols, or restructuring the query, using domain-specific lexicons to generate accurate search results without retraining the parser.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language parsers are used, then the system is simple and fast, but parsing quality is insufficient for applications with low tolerance for misinterpretation
Solution Approach 1:
The system implements feedback by detecting parsing ambiguities and presenting multiple possible interpretations to the user for selection. This feedback loop allows the system to identify when its initial parsing is uncertain and enables the user to guide the correct interpretation, thereby improving parsing quality and reducing misinterpretations without requiring complex retraining processes
Solution Approach 2:
The ambiguity detection and resolution mechanism acts as an intermediary between the parser and the user. When the parser encounters ambiguous queries, this intermediary component intervenes by presenting the ambiguity to the user and incorporating user feedback to resolve it, thereby bridging the gap between automated parsing and human understanding without requiring system retraining
2Measurement precision
If parser retraining and high quality training data are provided, then parsing quality is improved, but resource consumption and time requirements increase
Solution Approach 1:
The system provides self-service by automatically detecting parsing ambiguities and presenting resolution options to users without requiring external retraining interventions. The parser continuously improves through user feedback on ambiguous cases, eliminating the need for periodic resource-intensive retraining processes while maintaining high parsing quality
Solution Approach 2:
The system performs preliminary ambiguity detection and resolution during the parsing process itself, rather than relying on preliminary retraining phases. By identifying and resolving ambiguities in real-time through user interaction, the system achieves high parsing quality without the time loss associated with pre-training and retraining cycles
3Measurement precision
If parser retraining and high quality training data are provided, then parsing quality is improved, but resource consumption increases
Solution Approach 1:
The system improves parsing quality through self-service mechanisms that detect ambiguities and incorporate user feedback during operation, eliminating the need for resource-consuming retraining processes. This approach maintains high parsing accuracy while significantly reducing computational resource consumption compared to traditional retraining methods
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for disambiguating join paths for natural language queries. One of the methods includes, obtaining a natural language query from a user; parsing the natural language query into structured operations to be performed on APIs of a knowledge base, including: responsive to detecting a parsing ambiguity in which the natural language query can be parsed in two or more ways: providing, through a user interface, to the user one or more information items identifying the parsing ambiguity; responsive to a user interaction with an information item: modifying the parsing in accordance with the user interaction to generate one or more structured operations; performing the one or more structured operations on the structured APIs of the knowledge base to determine one or more search results; and providing one or more search results to the user.