Free-text Entity Search Using Segmented Keyword Indexing
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Solution Overview
Problem
Current search algorithms face challenges in accurately identifying entity names within free-text documents when keywords are indexed, as they often misinterpret words based on context, leading to false matches due to the lack of contextual understanding.
Innovation Solution
A service provider server performs a coarse search using a keyword index to generate solution sets with similarity scores, considering edit distance and index positions to differentiate between entity names and contextual words, ensuring accurate identification of entity names within free-text data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword indexing is used to search free-text documents, then search efficiency is improved, but false matches increase due to lack of contextual understanding
Solution Approach 1:
The patent segments the search process into multiple stages: coarse search using indexed keywords to identify potential matches, followed by contextual analysis to verify entity names. This segmentation allows efficient keyword-based filtering while subsequent contextual verification eliminates false positives, resolving the contradiction between search efficiency and detection accuracy.
Solution Approach 2:
The patent introduces contextual analysis as an intermediary step between keyword matching and final entity identification. This intermediary process examines surrounding text, syntax, and semantic context to distinguish actual entity names from coincidental keyword occurrences, thereby maintaining search efficiency while dramatically improving detection accuracy.
2Speed
If simple keyword matching is used, then processing speed is improved, but precision of entity identification deteriorates
Solution Approach 1:
The search process is divided into a fast coarse search phase using simple keyword matching, followed by a more intensive contextual verification phase. This segmentation enables the system to quickly filter potential matches at high speed, then apply precise entity identification only to candidate results, maintaining overall processing speed while improving identification precision.
Solution Approach 2:
The patent applies full contextual analysis only to partial results from the initial keyword search rather than to all possible matches. This partial application of intensive processing maintains high processing speed for the majority of cases while achieving high precision for the candidate entities that require verification.
Data Source
AI summary
A system performs operations that include determining match text data based on a comparison between input text data and an index of keywords and identifying a set of match word tokens from the match text data. The operations further include determining one or more solution sets of input word tokens based on comparing the set of match word tokens and the input text data. Additionally, the operations include calculating a respective solution set score for each of the one or more solution sets of input word tokens based on the calculated similarity values. A match between the input text data and the match text data is determined based on the first solution set score satisfying a score threshold.