Term Disambiguation via Context Vectors and Frequency Analysis
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Solution Overview
Problem
Current methods for word sense disambiguation and abbreviation disambiguation in computational linguistics face challenges in accurately determining the meaning of ambiguous terms, particularly in clinical narratives where abbreviations and acronyms have multiple meanings, leading to ambiguity and inefficiency in text normalization.
Innovation Solution
A method that extracts and analyzes context and frequency of potential meanings for ambiguous terms using a knowledge set that defines associated concepts, including section headings, nearby concepts, and co-occurring concepts from multiple information sources, to disambiguate terms by determining the relationship and frequency of use, thereby selecting the most relevant meaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If dictionary-based methods are used for abbreviation disambiguation, then implementation is simple, but accuracy is insufficient for ambiguous terms with multiple meanings
Solution Approach 1:
The patent introduces context vectors as an intermediary between the ambiguous abbreviation and its potential meanings. These context vectors capture semantic information from surrounding text and serve as a mediator to determine the most appropriate meaning, resolving the contradiction between simple implementation and accurate disambiguation
Solution Approach 2:
The system changes the parameter of representation from static dictionary entries to dynamic context vectors that adapt based on surrounding text. This allows the same abbreviation to be disambiguated accurately across different contexts while maintaining a unified implementation approach
2Measurement precision
If supervised machine learning methods are used with manually curated examples, then disambiguation accuracy improves, but system complexity and manual effort increase significantly
Solution Approach 1:
The system performs self-service by automatically learning context representations from unlabeled text data through unsupervised embedding techniques. This eliminates the need for manual curation of training examples while maintaining high disambiguation accuracy, reducing both manual effort and system complexity
Solution Approach 2:
The patent pre-computes context vectors and embedding representations that capture semantic relationships before disambiguation is needed. This preliminary action creates a reusable knowledge base that simplifies the actual disambiguation process and reduces real-time computational complexity
3Ease of operation
If unsupervised clustering methods are used, then manual effort is reduced, but disambiguation accuracy decreases for terms with multiple valid meanings
Solution Approach 1:
The system changes from discrete clustering assignments to continuous context vector representations that capture nuanced semantic differences. This allows multiple valid meanings to be represented as distinct points in the vector space, maintaining accuracy while requiring minimal manual intervention
Solution Approach 2:
The patent transitions from one-dimensional clustering labels to multi-dimensional context vectors that encode rich semantic information. This dimensional expansion allows the system to distinguish between multiple valid meanings of ambiguous terms without requiring manual curation, resolving the contradiction between ease of operation and accuracy
4Measurement precision
If context and frequency analysis from multiple information sources is performed, then disambiguation accuracy improves, but computational time and processing complexity increase
Solution Approach 1:
The system pre-computes context vectors and frequency statistics from multiple information sources before disambiguation is needed. These pre-computed representations are stored and reused, eliminating the need for repeated analysis and significantly reducing processing time while maintaining high accuracy
Solution Approach 2:
The context vectors serve multiple functions simultaneously: they capture semantic context, encode frequency information, and represent disambiguation evidence. This multi-functionality eliminates the need for separate processing steps for each type of analysis, reducing overall computational time while improving accuracy through integrated analysis
Data Source
AI summary
Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.