CNN Document Embedding Model for Variable-Length Text Processing
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Solution Overview
Problem
Existing document embedding models face challenges in computational efficiency and accuracy, often relying on small subsets of words and failing to incorporate long-range semantic relationships, requiring significant computational power, and involving iterative optimization processes.
Innovation Solution
A document analysis system trains a machine-learned document embedding model using a convolutional neural network (CNN) architecture that processes word embeddings to generate document embeddings, allowing for efficient processing of variable-length documents and incorporating both short-range and long-range semantic relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If existing document embedding models use small subsets of words, then computational power is reduced, but accuracy in characterizing the document as a whole deteriorates
Solution Approach 1:
The document is segmented into multiple overlapping local windows or regions, each processed by the CNN to extract local features. These local embeddings are then aggregated to form the global document embedding, allowing the model to process the entire document without requiring excessive computational resources on the full document at once.
Solution Approach 2:
The patent transitions from processing the document as a single global entity to processing it as multiple local regions in a spatial dimension. The CNN operates on these local windows, and the results are combined through aggregation, effectively adding a spatial processing dimension that improves accuracy while controlling computational complexity.
2Measurement precision
If existing document embedding models incorporate long-range semantic relationships, then accuracy improves, but computational complexity increases
Solution Approach 1:
The document is divided into overlapping local windows that capture different regions and their contextual relationships. The CNN processes each window independently to extract local semantic features, and these features are aggregated to capture long-range relationships without requiring the model to process all word pairs simultaneously, thus reducing computational complexity.
Solution Approach 2:
The CNN performs preliminary feature extraction on local windows before aggregation. This preliminary processing captures local semantic relationships efficiently, and the aggregation step subsequently combines these local features to infer long-range relationships, avoiding the need for computationally expensive global relationship modeling.
3Measurement precision
If existing document embedding models use iterative optimization processes, then accuracy can be improved, but inference time increases
Solution Approach 1:
The model performs iterative optimization during the training phase to learn optimal parameters and patterns. Once trained, the model can generate document embeddings directly during inference without requiring additional iterative optimization, as the learning process has already captured the necessary patterns. This separates the computational burden of iterative optimization from the inference process.
Solution Approach 2:
The trained model serves itself by directly computing document embeddings during inference using the learned parameters, without requiring external iterative optimization processes. The model has internalized the optimization results during training and can now perform inference efficiently using these pre-learned representations.
Data Source
AI summary
A document analysis system trains a document embedding model configured to receive a set of word embeddings for an ordered set of words in a document and generate a document embedding for the document. The document embedding is a representation of the document in a latent space that characterizes the document with respect to properties such as structure, content, and sentiment. The document embedding may represent a prediction of a set of words that follow the last word in the ordered set of words of the document. The document embedding model may be associated with a convolutional neural network (CNN) architecture that includes one or more convolutional layers. The CNN architecture of the document embedding model allows the document analysis system to overcome various difficulties of existing document embedding models, and allows the document analysis system to easily process variable-length documents that include a variable number of words.