Natural Language Query Precision via Structured Translation
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Solution Overview
Problem
Current information retrieval systems face challenges in achieving high precision and recall for natural language queries, often resulting in overwhelming and irrelevant search results due to the complexity of user interfaces and the inability to effectively leverage both natural language queries and structured searches.
Innovation Solution
An information retrieval system that processes natural language queries by extracting features and generating structured queries, using natural language processing and Generative Adversarial Networks to automatically tag documents and filter results, allowing users to interact with a single text field while improving both recall and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system uses traditional full-text indexing for information retrieval, then the user interface remains simple, but the precision and recall of search results deteriorate due to inability to effectively leverage structured searches
Solution Approach 1:
The system introduces an intermediary component that automatically translates natural language queries into structured search queries. This mediator processes the user's natural language input, extracts relevant features and entities, and generates appropriate structured search syntax, thereby bridging the gap between simple user interaction and precise structured search capabilities without requiring users to learn complex query languages
Solution Approach 2:
The system dynamically changes the parameter representation of queries by transforming natural language text into structured query parameters. It extracts entities, relationships, and attributes from natural language and maps them to structured search parameters, enabling the search system to interpret and process queries with the precision of structured searches while maintaining natural language input simplicity
2Measurement precision
If the system implements both natural language processing and structured query generation, then search result precision improves, but the device complexity increases due to multiple processing components
Solution Approach 1:
The system merges the natural language processing and structured query generation functions into an integrated module. By combining entity recognition, relationship extraction, and query transformation capabilities into a unified processing pipeline, the system achieves high search precision while reducing the apparent complexity through consolidated architecture rather than separate independent components
Solution Approach 2:
The system performs preliminary processing of natural language queries by pre-extracting entities, relationships, and semantic features before generating the structured search query. This preliminary analysis phase prepares the query data in advance, enabling the subsequent structured query generation to proceed efficiently with already-processed information, thereby managing complexity through staged processing
3Ease of operation
If the system processes only natural language queries without structured query generation, then the ease of operation is maintained, but the productivity of information retrieval deteriorates due to overwhelming and irrelevant results
Solution Approach 1:
The intermediary translation layer automatically converts natural language queries into structured search queries, enabling the system to leverage the precision and efficiency of structured searches. This mediator process occurs automatically in the background, maintaining ease of operation for users while dramatically improving retrieval productivity by generating targeted, structured queries that return more relevant results faster
Solution Approach 2:
The system replaces the mechanical limitation of processing only one query type with an automated translation mechanism. Instead of requiring users to manually switch between natural language and structured query modes, or requiring complex manual query construction, the system automatically substitutes and transforms queries into the most effective format for the search engine, thereby improving retrieval productivity while maintaining operational simplicity
Data Source
AI summary
An embodiment of the invention may include a method, computer program product and system for processing a natural language query. An embodiment may include receiving text of the natural language query. An embodiment may include extracting a set of features from the text through natural language processing. An embodiment may include generating a structured query based on the set of features. An embodiment may include normalizing the text to create a normalized natural language query. An embodiment may include executing a search of a corpus via the structured query and the normalized natural language query. An embodiment may include returning results of the search.