Cluster-Based Word Vector Processing for Distributed Training
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Solution Overview
Problem
Existing natural language processing solutions face inefficiencies in large-scale word vector training, as current algorithms typically run on single computers, lacking the capability for distributed parallel processing.
Innovation Solution
A cluster-based word vector processing method and apparatus that utilizes multiple worker computers to obtain and train word vectors, with integration and update modules to aggregate and update word vectors across the cluster, enabling efficient large-scale training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If word vector training is performed on a single computer using existing algorithms, then the training process is simple to implement, but the training efficiency and scalability are limited
Solution Approach 1:
The patent segments the word vector training task into multiple independent worker nodes that can process different portions of the corpus in parallel. Each worker computer independently performs training operations on assigned data segments, enabling distributed parallel processing that significantly improves training efficiency while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent transitions from single-computer sequential processing to multi-computer parallel processing by adding the spatial dimension of distributed computing. This dimensional change allows simultaneous training operations across multiple machines, dramatically increasing productivity without proportionally increasing system complexity through standardized communication protocols
2Productivity
If distributed parallel processing is implemented across multiple worker computers, then training efficiency improves, but the complexity of coordinating and integrating results increases
Solution Approach 1:
The patent merges the training results from multiple worker computers through a centralized server that aggregates and integrates the distributed training outputs. This merging process consolidates the scattered results into a unified word vector model, improving efficiency through parallel processing while managing coordination complexity through centralized result integration
Solution Approach 2:
The patent introduces a server as an intermediary between worker computers that coordinates result collection and integration. This mediator handles the complexity of aggregating training results from multiple sources, allowing worker nodes to focus on efficient parallel training while the intermediary manages the coordination overhead
Data Source
AI summary
Embodiments of the present application disclose a cluster-based word vector processing method, apparatus, and device. Solutions are include: in a cluster having a server cluster and a worker computer cluster, in which each worker computer in the worker computer cluster separately reads some corpuses in parallel, extracts a word and context words of the word from the read corpuses, obtains corresponding word vectors from a server in the server cluster, and trains the corresponding word vectors, and the server cluster updates word vectors of same words that are stored before the training according to training results of one or more respective worker computers with respect to the word vectors of the same words.