Language-Independent Contextual Embedding via Character Coordinates
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Solution Overview
Problem
Conventional language processing systems face limitations in interpreting data due to high dimensionality of embedding spaces, leading to computational inefficiencies and loss of information, especially when translating between languages, which results in inaccurate semantic and functional relation modeling.
Innovation Solution
A system for language-independent contextual embedding of entities in documents, utilizing a tokenizer module, encoder module, transmutation module, and prediction module to generate character-based embeddings in a multi-dimensional hierarchical space, allowing for efficient and accurate interpretation across languages without requiring large labeled datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large dimensionality of embedding space is used to model complex relations between objects, then the semantic and functional relations can be captured more accurately, but computational efficiency decreases and computational power requirements increase
Solution Approach 1:
The patent segments the embedding process into character-level embeddings rather than word-level embeddings. By breaking down words into individual characters and creating embeddings for each character coordinate, the system reduces the dimensionality of the embedding space while preserving semantic information. This segmentation allows complex relations to be modeled through compositions of character-level representations rather than requiring high-dimensional word-level embeddings.
Solution Approach 2:
The patent introduces a new dimensional approach by using character coordinates (position information) as an additional dimension in the embedding space. Instead of increasing embedding dimensionality to capture semantic relations, the system uses the character position coordinates as a separate dimensional layer that, when combined with character embeddings, provides both semantic and positional information without requiring high-dimensional embeddings.
2Measurement precision
If large dimensionality of embedding space is used to model complex relations, then semantic relations can be captured, but loss of information occurs when reducing dimensionality
Solution Approach 1:
The patent segments information into two distinct components: character embeddings (capturing semantic meaning) and character coordinates (capturing positional information). By segmenting the representation this way, the system avoids information loss during dimensionality reduction because each component serves a specific purpose and can be independently optimized without compromising the other.
Solution Approach 2:
The patent uses character coordinates as an intermediary between the character embeddings and the final semantic representation. The coordinates serve as a mediator that provides positional context without requiring high-dimensional embeddings, thus preventing information loss while maintaining semantic accuracy.
3Device complexity
If conventional word-level embedding is used, then processing is simpler, but translation between languages leads to large amount of information loss due to different lexical analogy and semantical relations
Solution Approach 1:
The patent segments the embedding unit from word-level to character-level. By embedding individual characters rather than whole words, the system creates a language-agnostic representation where characters from different languages can be mapped to a common embedding space. This segmentation enables better cross-lingual transfer because character-level representations preserve sublexical patterns and morphological information that are language-independent.
Solution Approach 2:
The patent changes the fundamental parameter of embedding granularity from word-level to character-level. This parameter change transforms the embedding space into a more universal representation that transcends language-specific vocabulary differences, thereby reducing information loss during translation while maintaining manageable processing complexity.
Data Source
AI summary
Disclosed is a system for language-independent contextual embedding of entities in a document that includes sentences. The system has a database and a processing arrangement. The processing arrangement has a tokenizer module for tokenizing sentences to obtain tokens, an encoder module for determining character coordinate corresponding to the tokens, wherein the character coordinates corresponding to the tokens occur in a multi-dimensional hierarchical space. The system has a transmutation module for processing the character coordinates to generate contextual embeddings thereof in the multi-dimensional hierarchical space and a prediction module for memorizing sequential information pertaining to the contextual embeddings of the character coordinates.