Sentence Semantic Comparison via SVD Matrix Reduction
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Solution Overview
Problem
Current search engines face challenges in accurately representing and comparing natural language queries due to their inability to account for the order and logical relationships of words in sentences, leading to issues with precision and recall in retrieving relevant documents.
Innovation Solution
The solution involves representing sentences as graphs with nodes and edges to capture meaningful associations, converting these graphs into matrices, and using Singular Value Decomposition (SVD) to reduce dimensionality and optimize information content, allowing for semantic comparison of query sentences with stored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If bag of words model is used to represent sentences, then the representation is simple and computation is fast, but the order and logical relationships of words are not captured
Solution Approach 1:
The sentence is segmented into subject and object components based on semantic roles rather than just word order. This allows the system to capture logical relationships by identifying which noun phrase is the agent and which is the patient, resolving the limitation of bag-of-words models that treat all words equally regardless of their semantic function.
Solution Approach 2:
The patent introduces a new dimension to sentence representation by adding semantic role labels (subject, object) to the traditional word frequency vectors. This transforms the representation from a one-dimensional bag-of-words model to a multi-dimensional model that incorporates both lexical content and grammatical/semantic structure, enabling more accurate semantic comparison.
2Measurement precision
If word order is used to represent logical relationships, then semantic meaning is better captured, but the vector size grows exponentially
Solution Approach 1:
The patent extracts only the essential semantic components (subject and object) from sentences rather than considering all possible word order permutations. By focusing on the key semantic roles that carry the most information about logical relationships, the system achieves accurate semantic representation without the exponential growth in vector dimensionality that would result from modeling all possible word arrangements.
3Quantity of substance
If high recall is achieved using bag of words approach, then more relevant documents are returned, but precision is low due to irrelevant results
Solution Approach 1:
The patent applies local quality by differentiating between different semantic roles of words within the same document corpus. Instead of treating all occurrences of a word uniformly, the system identifies whether the word functions as a subject or object in each context, allowing for more precise matching that maintains high recall while improving precision by filtering out irrelevant semantic matches.
4Measurement precision
If relational approaches are used to achieve high precision, then relevant results are accurately identified, but recall is low due to missed relevant documents
Solution Approach 1:
The patent creates a universal semantic representation framework that can handle both precise matching (like relational approaches) and comprehensive retrieval (like bag-of-words). By representing sentences in a standardized format that captures semantic roles, the system can simultaneously achieve high precision through accurate semantic comparison and high recall by efficiently comparing the compact representation against large document corpora.
Data Source
AI summary
Systems and methods for performing logical semantic sentence comparisons and sentence-based searches. Training is performed by running an NLP pipeline on unstructured text comprising sentences and creating sentence matrix representations on the unstructured text; storing the matrix representations in an indexed database; combining the stored matrix representations; running an SVD on the combined matrix; storing the SVD components in the indexed database; reiterating through the output of the NLP pipeline the sentences of the unstructured training text to form a low-dimensional matrix conversion for each sentence for storage in the database based on the calculated SVD components. Subsequent query statements are run through the same process based and converted into low-dimensional matrix representations using the SVD components from training; the low-dimensionality query matrix is compared to the stored low-dimensional matrices to determine the closest relevant documents, that are returned to the user.