Hierarchical Document Clustering via Semantic Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current document analysis systems face inefficiencies in organizing and searching large volumes of electronic documents due to reliance on keyword-based approaches that fail to account for semantics, leading to irrelevant results and poor contextual understanding.
Innovation Solution
The implementation of a system that generates an induced organizational hierarchy based on embeddings of document portions using techniques like smoothed inverse frequency and word embedding vectors, followed by hierarchical clustering to create semantically meaningful clusters, allowing for better document organization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based approaches are used for document organization and search, then the system is simple to implement and operate, but the semantic understanding and relevance accuracy deteriorate
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search mechanisms with semantic embedding vectors that capture contextual meaning. Document portions are transformed into dense vector representations using smoothing and embedding techniques, enabling the system to understand semantic relationships rather than relying on simple keyword matching. This substitution resolves the contradiction by maintaining operational simplicity while dramatically improving semantic understanding accuracy.
Solution Approach 2:
The patent transforms document portions from discrete keyword parameters into continuous embedding vector parameters. By applying smoothing techniques (e.g., TF-IDF weighting) and embedding layers, the system converts sparse keyword representations into dense semantic vectors that capture nuanced meaning. This parameter transformation enables better semantic understanding while preserving the ease of document organization through hierarchical clustering of these transformed parameters.
2Measurement precision
If hierarchical clustering based on embeddings is implemented, then document clustering accuracy and semantic meaning are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments documents into smaller portions (e.g., paragraphs, sentences, or phrases) before generating embeddings and performing hierarchical clustering. This segmentation reduces the computational complexity of processing entire documents while preserving semantic meaning in the clustered portions. The hierarchical structure naturally organizes these segments from fine-grained to coarse-grained clusters, improving accuracy without overwhelming system resources.
Solution Approach 2:
The patent applies smoothing and embedding transformations to document portions before clustering, creating pre-processed semantic representations that capture meaning upfront. This preliminary action reduces the complexity of the actual clustering operation, as the embeddings already encode semantic relationships. The hierarchical clustering then operates on these pre-computed vectors rather than raw text, significantly reducing computational requirements while maintaining high clustering accuracy.
3Use of energy by moving object
If traditional keyword search is used, then the system requires minimal computational resources, but the ability to identify relevant documents based on context and semantics deteriorates
Solution Approach 1:
The patent replaces energy-efficient but semantically limited keyword search with embedding-based semantic search. Document portions are converted into dense vectors using smoothing and embedding techniques, allowing the system to identify relevance based on contextual meaning rather than exact keyword matches. This substitution improves reliability of relevance identification while the hierarchical clustering structure helps manage computational resources by organizing documents into manageable semantic groups.
Solution Approach 2:
The patent applies embedding transformations and hierarchical clustering to only the most relevant document portions rather than processing entire document corpora uniformly. By identifying and embedding key portions (e.g., topic sentences, abstracts, or highlighted segments), the system achieves high relevance identification accuracy without the excessive computational cost of processing every word in every document. This partial action approach balances resource consumption with improved semantic understanding.
Data Source
AI summary
Embodiments as disclosed may generate an organizational hierarchy based on embeddings of portions of documents. Embeddings resulting from the embedding of the portions of the documents can be clustered using a hierarchical clustering mechanism to segment the portion space into a set of hierarchical clusters. Documents can be assigned to these clusters based on the presence of a portion of a document within a cluster. In this manner, the documents may themselves be clustered based on the clusters created from portions across the documents of the corpus. The clusters to which a document is assigned may also be ranked with respect to that document. Similarly, documents assigned to cluster can be ranked within the cluster to which they are assigned. Additionally, in certain embodiments, names or snippets for the clusters of the hierarchy may be derived from the portions comprising that cluster.