Document Ranking Using Negation Term Extraction
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Solution Overview
Problem
Relevancy ranking algorithms in search engines often produce false negatives and false positives, and require complex grammar and syntax parsing, leading to significant processing time and inefficient document retrieval.
Innovation Solution
A document ranking system that includes a term frequency module, negation module, and ranking module to identify key terms, exclude negated terms, and calculate relevancy rankings, using negation rules to determine context and assign tags to tokens for accurate document ranking and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex grammar rules and syntax parsing are used to rank documents, then the relevancy ranking accuracy is improved, but the processing time increases significantly
Solution Approach 1:
The patent extracts and removes negation terms from the document text before performing ranking operations. By identifying and eliminating words like 'not', 'never', 'without' and their contextual variations, the system simplifies the input data for ranking algorithms, reducing processing complexity while maintaining ranking accuracy.
Solution Approach 2:
The patent performs preliminary processing by identifying and tagging negation terms before the main ranking operation. This preliminary action of removing negation context prepares the data in advance, allowing subsequent ranking algorithms to work with simplified information, thereby reducing overall processing time without sacrificing accuracy.
2Productivity
If traditional relevancy ranking algorithms are used, then documents are ranked based on search terms, but false positives and false negatives occur
Solution Approach 1:
The patent converts the potential harm of negation terms (which can cause false positives by making relevant documents appear irrelevant) into a benefit by systematically identifying and removing these terms. This transformation allows the ranking system to avoid false positives while maintaining true positive detection, thereby improving both accuracy and reliability.
Solution Approach 2:
The patent introduces an intermediary processing step that acts as a mediator between the raw document text and the ranking algorithm. This intermediary layer identifies and removes negation terms, providing cleaned input to the ranking system and preventing false positives without requiring changes to the core ranking algorithm.
3Adaptability or versatility
If negation terms are included in document analysis, then comprehensive search coverage is achieved, but false positives increase
Solution Approach 1:
The patent extracts and removes negation terms from the document text before ranking. By taking out words like 'not', 'never', 'without' and their contextual variations, the system eliminates the source of false positives while preserving the ability to identify truly relevant documents, thereby improving ranking precision.
Solution Approach 2:
The patent performs preliminary removal of negation terms before the ranking process. This advance preparation prevents negation-related false positives from affecting the ranking results, ensuring that documents are ranked based on their actual relevance rather than being misled by negation context.
Data Source
AI summary
Systems and methods are provided for ranking document data retrieved from a data source in response to a search request. A ranking system retrieves document data from documents in the data source that each includes at least one key term that matches a search term in the search request. For each document, a term frequency value is calculated based on a number of occurrences of the key term in the document. Prefix and suffix term rules are used to determine whether a particular occurrence of the key term in a particular document should be included in determining a term weight value for that particular occurrence of the key term. A relevancy ranking value is determined for each document based on the corresponding term frequency and term weight values. The document data is displayed according to each document's corresponding relevancy ranking value.