Semantic Text Classification Training for Long Input Limits
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Solution Overview
Problem
Text classification models have a maximum input limit, such as 512 tokens, making it impractical to train them using long texts exceeding this limit without increasing memory and processing time exponentially, and they lack context awareness in classification.
Innovation Solution
Segment long texts into smaller segments, generate embeddings for each segment, compare them with embeddings of known semantics, and use these associations to train a text classification model using supervised learning, incorporating semantic context for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If long text is segmented into smaller segments to fit input limit, then the text can be processed by the model, but the context awareness and semantic understanding are lost
Solution Approach 1:
The long text is divided into multiple segments that fit within the model's input limit. Each segment is processed independently to generate embeddings, which are then aggregated to represent the entire long text while preserving semantic information.
Solution Approach 2:
The embeddings from multiple segmented text pieces are merged or aggregated into a single representation that captures the overall semantic meaning of the long text, thereby restoring context awareness that was lost during segmentation.
2Loss of information
If the input text length is increased beyond the model limit, then more context information is available, but the model cannot process it due to memory and processing time constraints
Solution Approach 1:
Instead of feeding the entire long text at once, the text is segmented into manageable chunks that fit within the model's input constraints, allowing processing without exceeding memory and computational limits.
Solution Approach 2:
Embeddings serve as an intermediary representation that compresses the semantic information of long text segments into a compact form, enabling the model to process and understand long texts indirectly through these embedded representations without directly handling the full text burden.
3Productivity
If traditional text classification is used without semantic analysis, then processing is faster and simpler, but classification accuracy on long texts deteriorates
Solution Approach 1:
Traditional mechanical text processing is replaced with semantic embedding-based processing. The embedding model captures semantic meanings of text segments, and these embeddings are used for classification, substituting direct text analysis with a more sophisticated semantic understanding approach.
Solution Approach 2:
The approach changes from direct text classification to embedding-based classification. By transforming text into embedding vectors and performing classification in the embedding space rather than raw text space, the system achieves better accuracy while maintaining reasonable processing efficiency.
Data Source
AI summary
Systems and methods are disclosed for training a text classification model based on long text and known semantics as training data. With a text classification model limited as to the amount of text that may be input at one time, long text that is greater than the limit may be segmented into smaller segments that are less than the limit (such as into sentences). Each segment of the long text is compared with sample segments with known impact of specific semantics to associate the long text segments with the specific semantics. To compare the long text segments with sample segments, an embedding model generates an embedding from each of the segments so that the embeddings may be compared. With the long text segments associated with specific semantics, the long text segments and the associated semantics are used as training data to train a text classification model.