Word Embedding via High-Dimensional Vector Mapping
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Solution Overview
Problem
Existing systems are inefficient in predicting unknown words in a sequence by converting words into numeric representations, as they fail to effectively capture semantic and syntactic similarities, and are limited by smaller training datasets and lower-dimensional representations.
Innovation Solution
A system with a two-layer architecture that uses an embedding function to map words into high-dimensional numeric representations, allowing classifiers to generate word scores based on surrounding words, and trains on large datasets to capture semantic and syntactic similarities, enabling effective prediction of unknown words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If words are converted into numeric representations using existing systems, then word prediction can be performed, but the systems fail to effectively capture semantic and syntactic similarities
Solution Approach 1:
The patent transitions from low-dimensional word representations to high-dimensional vector spaces (e.g., thousands of dimensions), allowing each dimension to capture different aspects of word meaning, context, and relationships. This dimensional expansion enables the model to preserve both semantic and syntactic similarities while improving prediction accuracy.
Solution Approach 2:
The patent changes key parameters including: (1) increasing vector dimensionality from traditional low dimensions to high dimensions; (2) changing the training approach from supervised classification to unsupervised continuous training on large corpora; (3) modifying the representation format to continuous high-dimensional vectors. These parameter changes enable effective capture of linguistic similarities.
2Productivity
If systems use smaller training datasets and lower-dimensional representations, then computational resources are conserved, but prediction effectiveness is limited
Solution Approach 1:
The patent performs preliminary unsupervised training on massive corpora (billions of words) before any prediction tasks. This pre-training phase builds comprehensive semantic and syntactic knowledge in the high-dimensional space, enabling effective predictions without requiring large labeled datasets for each specific task.
Solution Approach 2:
The patent creates a comprehensive copy of linguistic knowledge by training on extensive corpora, capturing diverse word usage patterns, contexts, and relationships. This large-scale pre-training creates a rich representation that can be applied to various prediction tasks without needing task-specific large datasets.
3Measurement precision
If high-dimensional representations and large datasets are used, then word prediction accuracy improves, but training time increases
Solution Approach 1:
The patent employs continuous training approaches where the model is trained incrementally on large corpora in a streamlined fashion. The continuous nature of the training process on unstructured text data allows efficient utilization of computational resources while building comprehensive representations, reducing overall training time compared to batch processing approaches.
Solution Approach 2:
The patent replaces traditional discrete, rule-based linguistic analysis with continuous high-dimensional vector representations. This substitution allows parallel processing and efficient computation of semantic relationships, significantly reducing training time while maintaining or improving accuracy compared to discrete symbolic methods.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.