Code-Mixed NLP Query Processing with AI Entity Inference
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Solution Overview
Problem
Conventional data processing techniques struggle with processing and interpreting natural language queries that contain multiple languages, often resulting in confusing and inaccurate outputs due to their single-language context.
Innovation Solution
The method involves detecting multiple languages in an input query, identifying and updating missing entity arguments by inferring data from partial query signals using artificial intelligence techniques, and generating coherent backend queries through domain reasoning and semantic interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-language data processing techniques are used, then the system is simple to implement, but the processing accuracy and coherence of code-mixed queries deteriorates
Solution Approach 1:
The system segments the code-mixed query into distinct language components (e.g., Hindi and English parts) and processes each segment separately using language-specific techniques, then integrates the results. This allows accurate handling of each language while maintaining overall system manageability.
Solution Approach 2:
The patent introduces an intermediary translation layer that converts code-mixed queries into a unified backend query language. This mediator handles the complexity of multi-language processing internally while presenting a simplified interface to users, resolving the contradiction between accuracy and complexity.
2Loss of information
If artificial intelligence techniques are used to infer missing entity arguments, then the completeness of query understanding improves, but the computational time and resources increase
Solution Approach 1:
The system applies AI techniques selectively only to portions of the query where entity arguments are missing or ambiguous, rather than processing the entire query uniformly. This partial application of AI inference reduces computational overhead while still recovering lost information where needed.
Solution Approach 2:
The system performs preliminary processing to identify and extract clearly stated entity arguments before applying AI inference techniques. By handling obvious cases upfront and reserving AI inference only for ambiguous cases, the system minimizes processing time while maintaining query completeness.
Data Source
AI summary
Methods, systems, and computer program products for automated code-mixed natural language processing for artificial intelligence-based question answering techniques are provided herein. A computer-implemented method includes detecting multiple languages in an input query to an artificial intelligence-based question answering system; determining, in the input query, one or more partial query signals associated with each of the multiple languages; identifying one or more missing entity arguments from at least a portion of the one or more partial query signals; updating at least a portion of the one or more missing entity arguments by inferring data from at least a portion of the one or more partial query signals using at least one artificial intelligence technique; and performing one or more automated actions based at least in part on the updating of at least a portion of the one or more missing entity arguments.