Semantic Text Comparison Using AI Topic Clustering
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Solution Overview
Problem
Existing natural language processing methods for summarizing and comparing documents often produce inaccurate, incomplete, or unacceptable results due to variations in quality and content, particularly in identifying semantic topics and maintaining computational efficiency.
Innovation Solution
A computer-implemented method using contextual word embeddings clustered into topics, with a neural network model classifier to determine topic correspondence values and generate similarity values by assigning comparison documents to matching clusters, incorporating cluster weights and relative weight proportions for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If unsupervised learning methods (Language Models, Autoencoders, Generative Adversarial Networks) are used to generate document summaries and translations, then the system can process documents automatically, but the output quality varies widely and produces inaccurate, incomplete, or unacceptable results
Solution Approach 1:
The patent introduces a two-stage intermediary system: first, a clustering algorithm groups embeddings into topics; second, a neural network classifier compares clusters between documents. This intermediary structure mediates between raw embeddings and final similarity assessment, improving reliability by adding a verification layer that checks whether compared embeddings actually represent corresponding topics.
Solution Approach 2:
The system implements feedback through the neural network classifier that evaluates topic correspondence between reference and comparison documents. The classifier receives cluster representations and feedback signals about topic matching, adjusting its comparisons to ensure only semantically equivalent topics are considered, thereby improving output quality consistency.
2Measurement precision
If document comparison methods group semantic topics into clusters with representative embeddings, then topic correspondence can be identified, but computational resources and processing time increase
Solution Approach 1:
The patent segments the document comparison task into distinct stages: (1) embedding generation, (2) clustering into topics, (3) representative embedding selection, and (4) neural network-based similarity assessment. This segmentation allows each stage to be optimized independently, improving precision while managing computational complexity through structured processing.
Solution Approach 2:
The system performs preliminary actions by pre-clustering embeddings into topics and pre-selecting representative embeddings before final comparison. This preliminary organization of data into semantic groups reduces the computational burden of subsequent similarity assessments, as the neural network only needs to compare pre-organized cluster representations rather than processing all individual embeddings.
3Measurement precision
If the system uses neural network classifiers trained on intra-cluster and inter-cluster embeddings, then topic correspondence accuracy improves, but training data requirements and model complexity increase
Solution Approach 1:
The patent applies local quality by training the neural network classifier on localized examples of intra-cluster and inter-cluster embeddings rather than using generic training data. The model learns specific patterns from actual cluster comparisons, improving accuracy for the specific task of topic correspondence while keeping the model architecture relatively simple and focused on the actual comparison need.
Data Source
AI summary
A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.