Natural Language Query Conversion via Context-Free Grammar and LSTM
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Solution Overview
Problem
Current systems face challenges in processing natural language input efficiently, particularly in resolving ambiguity and converting it into structured queries suitable for database search, due to limitations in context-free grammars and difficulty in handling nuanced user queries.
Innovation Solution
The system employs machine learning models, such as LSTM-based recurrent neural networks, in conjunction with context-free grammars and fuzzy matching techniques, to generate structured queries by leveraging large datasets and domain knowledge, thereby improving the processing of natural language inputs and resolving ambiguities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If context-free grammars are used for natural language processing, then the system structure is simple, but the ability to resolve ambiguity and handle nuanced queries is insufficient
Solution Approach 1:
The patent combines context-free grammar with machine learning models (specifically LSTM-based recurrent neural networks) to create a hybrid system. The CFG provides structural framework while the ML model handles ambiguity resolution and nuanced query understanding, achieving both simplicity and adaptability.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates natural language input through CFG rules into structured representations, then uses machine learning models to resolve ambiguities. This intermediary structure allows the system to maintain grammatical simplicity while adding sophisticated interpretation capabilities.
2Measurement precision
If machine learning models are used to convert natural language to structured queries, then the processing accuracy is improved, but the processing time increases
Solution Approach 1:
The patent applies context-free grammar rules in advance to structure the natural language input before it reaches the machine learning model. This preliminary structuring reduces the complexity of the ML processing needed, thereby improving accuracy while minimizing additional processing time.
Solution Approach 2:
The patent segments the natural language processing task into distinct phases: initial CFG-based structural analysis followed by targeted ML-based ambiguity resolution. This segmentation allows each component to focus on specific aspects, improving overall accuracy without requiring the entire system to process all aspects at full complexity.
3Adaptability or versatility
If fuzzy matching techniques are applied, then the handling of nuanced queries is improved, but the computational complexity increases
Solution Approach 1:
The patent applies fuzzy matching techniques selectively only to portions of the query where ambiguity or nuance is detected, rather than applying them uniformly to the entire natural language input. This localized application maintains adaptability for nuanced queries while reducing overall computational complexity.
Data Source
AI summary
The subject technology provides for converting natural language input to structured queries. The subject technology receives a user input query in a natural language format. The subject technology determines scores for candidate entities derived from the user input query. The subject technology selects an entity with a highest score among the candidate entities, and converts, using a context-free grammar, the user input query to a structured query based at least in part on the selected entity. The subject technology classifies the structured query to an expected answer type, the expected answer type corresponding to a type of an expected answer of the structured query. The subject technology queries a database based on the expected answer type and the structured query, the database including information corresponding to a knowledge graph. The subject technology provides, for display, an answer to the user input query based on a result of querying the database.