Self-Supervised Document Similarity via Hierarchical Matrix
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Solution Overview
Problem
Current methods for ranking and scoring documents are limited to short documents with similarity labels, making them ineffective for documents of arbitrary length and those lacking manual labels, which is a common challenge in real-world applications.
Innovation Solution
A self-supervised language model is pre-trained using intra-and-inter document sampling to create a two-staged hierarchical similarity matrix, allowing for accurate semantic similarity scoring and ranking of variable length documents without reliance on similarity labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Transformer language models are used for document comparison, then text representation effectiveness is improved, but maximum input text length is limited
Solution Approach 1:
The patent segments long documents into smaller units (sentences or paragraphs) that can be processed by Transformer models. It creates hierarchical representations where sentence embeddings are generated first, then aggregated to form document-level embeddings, enabling the model to handle arbitrary document lengths while maintaining effective text representation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the embedding process. Instead of directly embedding entire long documents, it creates a two-level hierarchy: sentence-level embeddings and document-level embeddings. This dimensional transformation allows the model to capture both local sentence semantics and global document meaning, resolving the length limitation.
2Measurement precision
If supervised learning with ground-truth labels is used for document ranking, then ranking accuracy is improved, but availability of similarity labels is reduced
Solution Approach 1:
The patent implements self-supervised learning where the model generates its own training labels from the document corpus itself. By using contrastive learning with positive pairs (similar documents) and negative pairs (dissimilar documents) automatically identified from the data, the system eliminates the need for manual ground-truth labels while maintaining ranking accuracy.
Solution Approach 2:
The patent changes the learning paradigm from supervised to self-supervised by modifying the objective function. It uses contrastive loss that operates on embedding similarities rather than requiring labeled outcomes, transforming the problem from classification to metric learning, thereby eliminating dependency on labeled data.
3Adaptability or versatility
If document-level matching techniques are developed, then applicability to long documents is improved, but current model reliability is reduced
Solution Approach 1:
The patent performs preliminary processing of long documents into sentence embeddings before document-level comparison. This preprocessing step creates standardized, fixed-dimensional representations that are reliable inputs for the similarity computation, ensuring consistent and reliable results regardless of the original document length.
Data Source
AI summary
Examples provide a self-supervised language model for document-to-document similarity scoring and ranking long documents of arbitrary length in an absence of similarity labels. In a first stage of a two-staged hierarchical scoring, a sentence similarity matrix is created for each paragraph in the candidate document. A sentence similarity score is calculated based on the sentence similarity matrix. In the second stage, a paragraph similarity matrix is constructed based on aggregated sentence similarity scores associated with the first candidate document. A total similarity score for the document is calculated based on the normalize the paragraph similarity matrix for each candidate document in a collection of documents. The model is trained using a masked language model and intra-and-inter document sampling. The documents are ranked based on the similarity scores for the documents.