Acronym Extraction via Context-Aware Ranking
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Solution Overview
Problem
Existing systems for acronym expansion, such as AcronymFinder, are inefficient due to manual submissions and lack of context awareness, leading to inaccurate and incomplete expansions, causing confusion and requiring additional effort from users.
Innovation Solution
A computer-implemented method that extracts and ranks acronym expansions from documents using context-aware algorithms, leveraging features of markup languages like HTML and XML, including tags and links, to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual submission systems are used to compile acronym lists, then the system can be simple to implement, but the efficiency and productivity of acronym extraction is poor
Solution Approach 1:
The system automatically extracts acronyms and their expansions from text documents using computational algorithms, eliminating the need for manual submission and compilation of acronym lists. The algorithm autonomously identifies acronym patterns, extracts corresponding expansions from context, and builds the acronym dictionary without human intervention.
Solution Approach 2:
The patent replaces the manual mechanical process of acronym compilation with an automated computational system. The algorithm uses text processing and pattern recognition techniques to automatically identify acronyms, extract their meanings from surrounding context, and organize them into a structured dictionary, substituting human effort with machine-based automated extraction.
2Measurement precision
If generic static acronym lists are used, then the system is easy to maintain, but the accuracy and relevance of acronym expansions is poor
Solution Approach 1:
The system extracts acronym expansions from local context within the document, analyzing the surrounding text to determine the specific meaning of each acronym in its particular context. This allows the same acronym to have different expansions depending on the local context, improving accuracy for domain-specific and organization-specific acronyms.
Solution Approach 2:
The acronym dictionary is dynamically generated from the actual document corpus rather than being static and pre-defined. The system adapts to different domains and organizations by automatically learning the specific acronyms and their meanings from the documents it processes, making the system versatile and context-adaptive.
3Measurement precision
If automated acronym extraction is implemented, then productivity improves, but the accuracy of acronym expansions deteriorates due to lack of context awareness
Solution Approach 1:
The system uses context feedback from the surrounding text to refine and verify acronym expansions. By analyzing the local context, word co-occurrences, and document structure, the algorithm feedback-adjusts the extracted expansions to ensure they accurately reflect the intended meaning, thereby maintaining high accuracy while operating automatically.
Solution Approach 2:
The patent introduces context analysis as an intermediary layer between automatic acronym identification and expansion selection. This intermediary process examines the surrounding text, document structure, and linguistic patterns to mediate the selection of the most appropriate expansion, ensuring accuracy is maintained while the system operates autonomously.
4Measurement precision
If context-aware automated extraction is used, then accuracy improves, but the complexity of the extraction algorithm increases
Solution Approach 1:
The extraction algorithm is segmented into distinct modular components: acronym pattern identification, candidate expansion extraction from context, context analysis, ranking/scoring of expansions, and dictionary construction. This segmentation manages complexity by breaking down the complex task into smaller, more manageable modules that can be developed and maintained independently.
Data Source
AI summary
Disclosed is a system and computer-implemented method for extracting an acronym and one or more corresponding expansions of the acronym from a document represented in a markup language. The computer-implemented method comprises: identifying at least one acronym contained in the document; determining one or more expansions of the at least one identified acronym based on a portion of document located proximate the identified acronym; determining a ranking for each determined expansion based attributes of the document; and selecting one or more expansions for an identified acronym using the determined rankings.