Text Element Specificity Ranking via Embedding Distance
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Solution Overview
Problem
Existing techniques for estimating the specificity of text elements rely heavily on statistical analysis of parts of speech and term frequency, which may not accurately capture the contextual homogeneity of highly specific terms.
Innovation Solution
A computer-implemented method that computes embedding vectors for text elements using a word embedding scheme, calculates distances in the embedding space between text elements and further text elements in context fragments, and uses these distances to determine a specificity score for each text element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statistical analysis of parts of speech and term frequency is used to estimate specificity, then the method is simple to implement, but the accuracy of capturing contextual homogeneity deteriorates
Solution Approach 1:
The patent replaces traditional statistical linguistic analysis (parts of speech, term frequency) with a word embedding-based vector space model. This substitution enables the system to capture contextual homogeneity through geometric relationships in embedding space, significantly improving specificity estimation accuracy while maintaining computational efficiency.
Solution Approach 2:
The patent transforms the specificity measurement approach by changing from discrete statistical parameters (term frequency counts, part of speech tags) to continuous vector representations in embedding space. This parameter transformation allows for more nuanced measurement of contextual similarity and specificity through distance metrics and vector operations.
2Productivity
If only vector distribution in pretrained embedding is used to assess specificity, then the computation is efficient, but the contextual information is insufficient
Solution Approach 1:
The patent segments the assessment process into multiple components: (1) extracting text fragments containing the target text element, (2) computing embedding vectors for all elements in fragments, (3) calculating distances between target element and other elements in fragments, and (4) aggregating these distances to compute specificity. This segmentation preserves rich contextual information while maintaining computational efficiency through modular processing.
Solution Approach 2:
The patent introduces text fragments as intermediary structures between the target text element and the embedding space analysis. These fragments serve as contextual containers that preserve local linguistic context, allowing the system to assess specificity based on homogeneous contextual patterns without requiring analysis of the entire corpus.
Data Source
AI summary
Ranking a plurality of text elements, each comprising at least one word, by specificity. For each text element to be ranked, such a method includes computing an embedding vector that locates a text element in an embedding space, and selecting a set of text fragments from reference text. Each of these text fragments contains the text element to be ranked and further text elements. For each text fragment, the method calculates respective distances in the embedding space between the further text elements. The method further includes calculating a specificity score for the text element to be ranked and storing the specificity score. After ranking the plurality of text elements, a text data structure using the specificity scores for text elements to extract data having a desired specificity from the data structure may be processed.