Named Entity Resolution in NL2LF for Date and Time Queries
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Solution Overview
Problem
Existing natural language to SQL (NL2SQL) systems face challenges in accurately capturing named entities, particularly date and time expressions, due to the need for large amounts of labeled training data and the complexity of deep-learning models, which limits their generalization across domains.
Innovation Solution
Utilizing a pretrained Named Entity Recognition (NER) model in conjunction with a Relation-Aware Transformer to recognize and resolve named entities, such as date and time expressions, reducing the need for extensive training data and simplifying the NL2LF model by obscuring implementation details from users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep-learning models are used for NL2SQL conversion, then the system can understand natural language queries, but large amounts of labeled training data are required and model complexity increases
Solution Approach 1:
The patent segments the NL2SQL task into distinct components: natural language processing, SQL generation, and execution. By separating these functions into modular components, the system reduces overall complexity while maintaining natural language understanding capabilities through specialized sub-models that can be trained independently with less data.
Solution Approach 2:
The patent introduces an intermediary layer between natural language input and SQL output, using structured representations and templates as intermediate forms. This intermediary structure simplifies the learning task for the model by providing a bridge that reduces the complexity of direct mapping while preserving language understanding capabilities.
2Adaptability or versatility
If deep-learning models are used for NL2SQL conversion, then the system can understand natural language queries, but the amount of training data required increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing natural language queries into structured representations before SQL generation. This preliminary structuring reduces the complexity of the learning task, allowing the model to achieve good performance with less training data since the heavy lifting of understanding has already been done in the preprocessing stage.
Solution Approach 2:
The patent changes parameters by transforming the problem from learning direct NL-to-SQL mapping to learning structured representation generation followed by template-based SQL construction. This parameter change in the learning objective reduces data requirements while maintaining language understanding capabilities.
3Measurement precision
If complex NL2LF models are used, then accurate entity resolution can be achieved, but implementation details become complex for users
Solution Approach 1:
The patent extracts and encapsulates complex implementation details into hidden layers of the system, exposing only simple natural language interfaces to users. The complex entity resolution and SQL generation logic is taken out from user view, allowing high accuracy while maintaining ease of operation through automatic processing.
Solution Approach 2:
The system performs self-service by automatically handling complex entity resolution and query generation without requiring user intervention or understanding of underlying complexity. Users simply input natural language queries and receive results, while the system autonomously manages the complex processing steps.
Data Source
AI summary
Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.