Word Embedding System Using Average and Max Pooling Vectors
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Solution Overview
Problem
Existing word embedding techniques for Natural Language Processing (NLP) are overly complex and cumbersome to implement, necessitating a simpler approach that leverages only word embedding information without compositional parameters.
Innovation Solution
A computer-implemented method and system that generates average and max pooling vectors from a word embedding matrix and applies a Multi-Layer Perceptron (MLP) to these vectors for prediction, eliminating the need for complex learning structures and compositional parameters, resulting in a more efficient and computationally simpler model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior techniques for word embedding are used, then NLP tasks can be performed, but the implementation becomes overly complex and cumbersome
Solution Approach 1:
The patent segments the word embedding process into distinct modular components: (1) obtaining word embeddings from pre-trained models, (2) generating average pooling vectors through element-wise averaging, (3) generating max pooling vectors through element-wise maximum selection, and (4) concatenating these vectors. This segmentation eliminates the need for complex compositional parameters while maintaining NLP task performance.
Solution Approach 2:
The patent extracts only the essential word embedding information from pre-trained models and discards complex compositional parameters. By taking out only the necessary embedding vectors and applying simple pooling operations, the system achieves effective NLP task performance without the burden of complex implementation structures.
2Adaptability or versatility
If complex learning structures with compositional parameters are used, then modeling capability is enhanced, but the number of parameters increases and overfitting risk increases
Solution Approach 1:
The patent uses simple, computationally inexpensive operations (element-wise averaging and maximum selection) instead of complex learning structures. These simple pooling operations require minimal parameters and can be applied efficiently without the need for extensive training data, thereby reducing overfitting risk while maintaining adequate modeling capability for various NLP tasks.
3Ease of manufacture
If simple word embedding models are used, then implementation is easier and parameters are fewer, but performance on NLP tasks may be compromised
Solution Approach 1:
The patent creates a composite representation by concatenating average pooling vectors and max pooling vectors. This composite approach combines the strengths of both pooling methods: average pooling captures overall semantic information while max pooling highlights salient features. The concatenated composite vector maintains adequate modeling capability for various NLP tasks while keeping the implementation simple and parameter-free.
Data Source
AI summary
A computer-implemented method, computer program product, and computer processing system are provided for word embedding. The method includes receiving, by a processor device, a word embedding matrix. The method further includes generating, by a processor device, an average pooling vector and a max pooling vector, based on the word embedding matrix. The method also includes generating, by the processor device, a prediction by applying a Multi-Layer Perceptron (MLP) to the average pooling vector and the max pooling vector.