Distributed NLP System Master-Slave Architecture
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Solution Overview
Problem
Current natural language processing (NLP) systems are inefficient and unable to scale to meet demand, lacking a user-friendly NLP-as-a-service model that can process raw text data to produce predetermined outputs effectively.
Innovation Solution
A distributed NLP system architecture that utilizes a master-slave node configuration within computing clusters to process natural language tasks in an ordered sequence, leveraging multiprocessing and a data structure (blade) to efficiently execute tasks such as sentence segmentation, word tokenization, and named entity recognition, with outputs transmitted as JSON objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current NLP systems use complex and redundant software architecture, then they can process natural language tasks, but they fail to scale efficiently and cannot provide NLP-as-a-service
Solution Approach 1:
The system segments NLP processing into distinct task types (tokenization, parsing, entity recognition, etc.) that can be independently selected and executed. Each task type is handled by specialized software components that can be activated only when needed, eliminating redundant processing and enabling efficient scaling through selective task execution.
Solution Approach 2:
The system creates a universal NLP platform that can handle multiple task types through a common architecture. The task graph framework and standardized data structures allow the same infrastructure to support diverse NLP tasks, enabling the system to provide scalable NLP-as-a-service across different applications without requiring separate complex systems for each task.
2Productivity
If NLP systems process massive volumes of unstructured text, then they can extract information, but they cannot meet demand due to inefficient hardware utilization
Solution Approach 1:
The system dynamically allocates hardware resources based on processing demand. The task graph execution engine can distribute tasks across available computing nodes, scaling hardware utilization up or down depending on the volume of text to be processed. This dynamic resource allocation allows the system to handle massive volumes of unstructured text efficiently without over-provisioning hardware.
3Adaptability or versatility
If NLP systems use redundant software components, then they can handle various tasks, but they cannot scale to meet demand
Solution Approach 1:
The system applies local quality by specializing different software components for specific task types. Instead of using generic redundant components, each task type (tokenization, parsing, entity recognition) has optimized software handling it. This specialization maintains versatility while improving scaling efficiency, as the system can activate only the specific task handlers needed for each processing request.
Data Source
AI summary
Disclosed are systems and methods for natural language processing. The method includes receiving, by a first processor, a natural language input including a raw text document and a desired output request, defining an ordered sequence of natural language tasks based at least in part on the natural language input, and generating a data structure including the raw text document. The first processor can then transmit, to a second processor, the data structure and a request to execute a first natural language task in the ordered sequence, and the second processor can execute the first natural language task based at least in part on the data structure to produce a first task output. The second processor can then insert the first task output into the data structure and transmit the data structure having the first task output to the first processor. The first processor can then generate an output based at least in part on the data structure and the desired output request.