Semantic Phrase Variation Detection Using Vector Embeddings
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Solution Overview
Problem
Current automated text analysis systems struggle to efficiently identify semantic variations of phrases in large, dynamic textual representations, such as those from call center conversations, due to limitations in computational intensity and the inability to capture higher resolution nuances.
Innovation Solution
The proposed solution involves using a neural network-based approach that calculates vector embeddings for each n-gram or word, allowing for distance calculations between search terms and text n-grams, thereby reducing computational costs and enabling effective identification of semantic variations without predefining topics or phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-encoder approach is used to detect phrase similarity, then semantic accuracy is improved, but computational complexity increases prohibitively
Solution Approach 1:
The patent segments the computational task by dividing the text into sentences and further into n-grams, then calculates embeddings independently for each segment. This avoids the need for computationally intensive cross-encoder pairwise comparisons while maintaining semantic accuracy through hierarchical set generation and embedding-based similarity calculation.
Solution Approach 2:
The patent pre-calculates and stores embedding vectors for all n-grams in the text before performing similarity searches. This preliminary action allows for efficient retrieval and comparison during query processing, eliminating the need for repeated forward passes through the neural network model.
2Productivity
If bi-encoder approach is used for phrase similarity detection, then computational efficiency is improved, but ability to capture token-level nuances is lost
Solution Approach 1:
The patent segments text into fine-grained n-grams (including individual tokens) and calculates embeddings for each segment independently. This segmentation enables token-level resolution while maintaining computational efficiency through vector space operations rather than full cross-encoder processing.
Solution Approach 2:
The patent transitions from discrete phrase matching to continuous vector space representation, where semantic similarity is measured through geometric distance metrics. This dimensional transformation enables efficient comparison while preserving fine-grained semantic distinctions at the token level.
3Speed
If traditional lexical matching is used to identify semantic similarity, then computational speed is improved, but recall decreases due to inability to capture synonyms and diverse articulations
Solution Approach 1:
The patent transforms the matching parameter from exact lexical equality to semantic similarity in vector space. By representing words and phrases as embedding vectors and measuring distance between them, the system captures synonyms and diverse articulations while maintaining computational efficiency through vector operations.
Solution Approach 2:
The patent replaces mechanical lexical matching operations with neural network-based embedding and vector space operations. This substitution enables the system to understand semantic meaning and recognize synonymous expressions without sacrificing computational speed.
4Measurement precision
If TF/IDF weighting is applied to evaluate word relevance, then importance of significant words is improved, but ability to capture semantic variations is reduced
Solution Approach 1:
The patent changes the parameter for evaluating word relevance from statistical frequency-based weights (TF/IDF) to semantic similarity in vector space. Embedding-based similarity captures the meaning and context of words, enabling the system to recognize semantic variations and synonyms that frequency-based methods miss.
Data Source
AI summary
A system and method of identifying occurrence of a semantic variation of a phrase in a passage by at least one processor may include calculating a phrase embedding vector, representing a semantic meaning of the phrase; extracting, from a textual representation of the passage, at least one hierarchical set of nested sequences of words; for each sequence, calculating a corresponding sequence embedding vector, representing a semantic meaning of the sequence; for one or more sequence embedding vectors, calculating a corresponding vector similarity value, representing similarity of the sequence embedding vectors to the phrase embedding vector, identifying a sequence corresponding to a maximal vector similarity value of the one or more vector similarity values; and determining the identified sequence as a semantic variation of the phrase, based on the maximal vector similarity value.