Latent Space Encoding for Long Text NER
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural language processing systems face inefficiencies and accuracy issues when processing text sequences of unspecified length, particularly exceeding 512 tokens, due to quadratic computational limitations of traditional attention mechanisms, leading to inadequate performance in extracting and classifying text-based information from images.
Innovation Solution
Implementing a transformer architecture that projects text to a latent space using a cross-attention mechanism and reversely projects it back to the input sequence space, enabling stage-wise processing and refinement, while filtering out irrelevant text to reduce computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional attention mechanisms are used for processing text sequences, then the system can process text of limited length (up to 512 tokens), but the computational complexity increases quadratically leading to inefficiency and inaccuracy for longer text sequences
Solution Approach 1:
The patent segments the text processing into two distinct spaces: input sequence space and latent space. The cross-attention mechanism processes text by projecting it to a latent space where computations occur, then reversing the projection. This segmentation allows the system to handle longer sequences efficiently by performing computations in the compressed latent representation rather than directly on the full input sequence, thereby reducing quadratic computational complexity while maintaining processing capability for extended text.
Solution Approach 2:
The patent introduces a latent space dimension that is distinct from the input sequence space. By projecting text embeddings into this additional latent dimension and performing attention operations there, the system transforms the computational problem from a quadratic complexity in the original sequence space to a more efficient computation in the latent space. This dimensional transformation enables the system to scale to longer text sequences without suffering from quadratic complexity penalties.
2Quantity of substance
If the text sequence length increases beyond 512 tokens, then more comprehensive information can be processed, but the quadratic computational limitations cause inadequate performance in extraction and classification
Solution Approach 1:
The latent space serves as an intermediary between the input sequence space and the processing mechanisms. Text embeddings are first projected into this intermediate latent space where the cross-attention mechanism performs its computations. This intermediary latent representation compresses and reorganizes the information from long text sequences, enabling the system to maintain processing accuracy for extended texts by operating on a condensed representation rather than the full sequence directly.
3Loss of information
If traditional attention mechanisms process long text sequences, then complete context can be captured, but the system becomes inefficient and time-consuming
Solution Approach 1:
The patent changes the fundamental parameter of where attention computations occur by introducing a separate latent space. Instead of computing attention directly on the input sequence embeddings (which yields quadratic time complexity), the system projects embeddings to latent space and performs attention there. This parameter change in the computational space allows the system to maintain complete context capture for long sequences while significantly reducing processing time through more efficient latent space operations.
Data Source
AI summary
Systems and methods are described for natural language processing of a text sequence. The system can identify a set of text and location information for the set of text in an image. The set of text may correspond to an input sequence space. The system can project embeddings of the text into a latent space for processing. Further, the system can reproject the processed embeddings from the latent space to the input sequence space. The system may perform multiple stages of projecting the embeddings to the latent space and reprojecting the processed embeddings from the latent space to the input sequence space. The system can route the reprojected embeddings to a neural network that can identify class predictions for elements of the set of text.