Dependency Graph NLP Pipeline Automation
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Solution Overview
Problem
Current natural language processing systems are inefficient, inaccurate, and not scalable to handle complex semantic models and large teams of developers, due to complexities in determining precise interpretations of natural language inputs and the need for numerous AI and ML models, which leads to increased processing time and costs.
Innovation Solution
The system generates a pipeline based on a dependency graph that organizes semantic and syntactic tags into a hierarchical structure, allowing for efficient processing by ensuring that dependee operations are completed before their dependers, and merges operations with similar functions to reduce the number of operations required, thereby optimizing resource usage and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple AI and ML models are used to process complex natural language text, then the accuracy and completeness of interpretation improves, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the natural language processing task into multiple independent pipelines, each handling specific aspects of text interpretation. These pipelines can be executed in parallel, reducing overall processing time while maintaining comprehensive analysis through the combination of multiple specialized models.
Solution Approach 2:
The system dynamically selects and configures pipelines based on the specific characteristics of the input text and the interpretation goals. This allows the system to adapt the processing complexity to match the actual needs, avoiding unnecessary computational overhead while maintaining accuracy.
2Adaptability or versatility
If the number of AI and ML models increases to handle complex natural language, then the coverage and sophistication of the NLP system improves, but the software development and maintenance complexity increases
Solution Approach 1:
The patent creates a universal pipeline management framework that can handle multiple AI and ML models through a common interface and configuration system. This framework provides unified tools for pipeline creation, execution, and maintenance, reducing the complexity burden despite having numerous specialized models.
Solution Approach 2:
The system uses template-based pipeline configurations that can be replicated and modified for different NLP tasks. Instead of manually configuring each pipeline from scratch, developers can copy and adapt existing pipeline templates, significantly reducing development and maintenance effort.
3Measurement precision
If ML classifiers require exact same input types for accurate prediction, then the classification accuracy improves, but the system becomes less flexible in handling variations of input data
Solution Approach 1:
The patent implements preprocessing operations at the beginning of each pipeline that standardize and normalize input data before it reaches the ML classifiers. This preliminary action ensures that all inputs meet the exact requirements of the classifiers while allowing the system to accept a wide variety of original input formats, thus resolving the contradiction between accuracy and flexibility.
Data Source
AI summary
Examples of automatically generating natural language pipelines to process an input to generate tags, semantic or syntactic, are described. In an example, on receiving a request to process input data to generate tags, a dependency graph, based on identified dependees and further dependees may be created to satisfy the request, the dependency graph including natural language operations arranged in order of their dependencies on each other. Based on the dependency graph, a pipeline for the tags may be automatically generated, which includes a series of natural language operations such that the operations for dependee tags are processed before any of their associated depender tags. Further, the dependency graph and the automated pipeline generation allows for automated optimization of the pipeline, training, re-training, testing and regression testing of the semantic tags and supporting machine learning models and provides a framework to efficiently manage the sharing and reuse of semantic understanding operations.