Contextual Abbreviation Expansion via Neural Vector Embeddings
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Solution Overview
Problem
In medical records, abbreviations like 'av' are context-dependent, making proper interpretation challenging due to data sparsity issues in traditional feature spaces, where words like 'narrowing,' 'stenosis,' and 'normal' are equally distant, and existing methods assume a fixed number of senses or expansions per word, which is not applicable in real-world data.
Innovation Solution
A neural network is trained to learn word vector representations that predict nearby words, using a projection layer to map inputs onto a continuous vector space, allowing for the identification and expansion of abbreviations based on surrounding context, and adjusting synaptic weights through backpropagation for accurate expansions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature spaces are used for abbreviation expansion, then the system is simple to implement, but data sparsity issues arise and context-dependent interpretation accuracy deteriorates
Solution Approach 1:
The patent transforms the traditional discrete feature space into a continuous vector space through neural network embeddings. Each word and abbreviation is represented as a dense vector capturing semantic meaning, allowing the system to generalize from limited training examples by leveraging the continuous geometric structure of the embedding space rather than relying on sparse discrete feature counts
Solution Approach 2:
The patent changes the parameter representation from discrete categorical features to continuous vector parameters through neural network embeddings. This transformation allows the system to capture nuanced contextual relationships and semantic similarities that discrete features cannot represent, improving interpretation accuracy while requiring less training data
2Measurement precision
If traditional feature spaces treat all words equally distant, then the feature space is uniform and simple, but semantic meaning differentiation is lost
Solution Approach 1:
The patent applies local quality by creating a feature space where each region reflects the semantic properties of nearby concepts. Words with similar meanings are positioned close together in the embedding space, while semantically distant words are positioned far apart. This local structuring allows the system to capture nuanced semantic differences while maintaining overall space coherence
Solution Approach 2:
The patent introduces continuous vector dimensions that capture semantic relationships, transforming the flat uniform feature space into a multi-dimensional semantic landscape. This allows the system to differentiate between words based on their semantic proximity in the continuous space rather than treating all words as equidistant discrete features
3Adaptability or versatility
If fixed number of senses per word is assumed, then the model is simpler to train, but real-world context-dependent variability is not captured
Solution Approach 1:
The patent implements a dynamic model where the number and identity of abbreviation expansions are not fixed but determined dynamically by the contextual embedding. The neural network continuously adapts to predict the most appropriate expansion based on the surrounding words' vector representations, allowing the system to handle variable numbers of senses naturally without requiring predefined constraints
Solution Approach 2:
The patent introduces continuous vector embeddings as an intermediary layer between the input abbreviation and its possible expansions. This intermediary representation captures the semantic context and enables flexible, context-dependent disambiguation without requiring the model to explicitly enumerate or predefine all possible senses, thereby increasing adaptability while managing complexity
Data Source
AI summary
Local abbreviation expansion is provided through context correlation. In various embodiments, an abbreviation within a phrase is identified. The abbreviation is surrounded by a plurality of words. The words surrounding the abbreviation are provided to a trained neural network. The neural network includes a projection layer adapted to map inputs of the neural network onto a continuous vector space. An expansion is received from the trained neural network. The expansion corresponds to the abbreviation based on the surrounding plurality of words.