Character Shape Embeddings for NLP Accuracy
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Solution Overview
Problem
Current word embedding models fail to capture both the contextual usage and shape properties of characters in natural language processing, leading to loss of important stylistic features and reduced accuracy in tasks like named entity recognition.
Innovation Solution
The creation of character shape embeddings in an artificial neural network by augmenting word representations using natural language processing data, word embedding vectors, and clustering operations, which captures both contextual usage and shape properties of characters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional word embedding models are used, then the implementation is simple and computationally efficient, but the character shape properties and stylistic features are lost
Solution Approach 1:
The patent segments the word representation problem into two distinct components: character-level embeddings (capturing shape properties) and word-level embeddings (capturing contextual usage). By dividing the embedding process into these separate segments that operate at different granularities, the model preserves character shape information that would otherwise be lost in traditional word-only embeddings, while maintaining computational feasibility through modular architecture
Solution Approach 2:
The patent adds a new dimension to the embedding space by introducing character shape embeddings as a separate feature dimension alongside traditional word embeddings. This dimensional expansion allows the model to represent both contextual meaning and visual shape properties simultaneously in a multi-dimensional vector space, resolving the information loss without requiring a complete redesign of the embedding architecture
2Measurement precision
If character shape embeddings are added to capture shape properties, then the representation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent merges character shape embeddings with word embeddings through a unified neural network architecture that processes both types of embeddings simultaneously. By combining these different embedding types in a integrated framework with shared layers and joint training, the model achieves improved representation accuracy while avoiding the overhead of completely separate processing systems, thus managing computational complexity
Solution Approach 2:
The neural network architecture is designed with universal components that can process both character-level and word-level embeddings through the same computational layers. This multi-functional design allows a single model structure to handle multiple embedding types and perform various NLP tasks, reducing overall system complexity compared to having specialized models for each embedding type
3Loss of information
If clustering operations are performed to group similar characters, then the shape property capture improves, but the processing time increases
Solution Approach 1:
The patent performs clustering operations during the offline preprocessing stage to create character shape embeddings, rather than during online inference. By conducting the computationally intensive clustering and grouping of similar characters in advance, the stylistic feature information is captured and stored in pre-computed embedding vectors, allowing fast retrieval during actual NLP tasks without adding processing time to the critical inference path
Data Source
AI summary
Embodiments are provided for intelligent augmentation of word representations in a computing system by a processor. One or more character shape embeddings may be created in an artificial neural network (ANN) by augmenting one or more word representations based on a set of natural language processing (NLP) data, a set of word embedding vectors, and one or more clustering operations. A shape, a category, and a contextual identify of the one or more word representations may be analyzed used in creating the one or more character shape embeddings.